ISSN 2379-5980 (online)
A Taxonomy of Blockchain Technologies:
Principles of Identiﬁcation and Classiﬁcation
Paolo Tasca,∗†Claudio J. Tessone‡
Abstract. A comparative study across the most widely known blockchain technologies is
conducted with a bottom-up approach. Blockchains are deconstructed into their building
blocks. Each building block is then hierarchically classiﬁed into main and subcomponents.
Then, varieties of the subcomponents are identiﬁed and compared. A taxonomy tree is used
to summarise the study and provide a navigation tool across different blockchain architectural
1.1. Background—The original combination of a set of existing technologies (distributed
ledgers, public-key encryption, merkle tree hashing, consensus protocols) gave origin to the
peer-validated decentralised cryptocurrency called Bitcoin, originally introduced by Satoshi
Nakamoto, in 2008.
That year was the advent of a new technological milestone: the blockchain.
Indeed, blockchains have an impact well beyond the speciﬁc case of Bitcoin. Blockchains allow
new forms of distributed software architecture to be developed where networks of untrusted
(and sometimes even “corrupted”) participants can establish agreements on shared states for
decentralised and transactional data in a secure way and without the need for a central point of
control or regulatory supervision. Blockchains ensure trust among anonymous counterparts in de-
centralised systems without the need of central supervisory authorities in charge of verifying the
correctness of the records in the ledger. Blockchains were announced as a disruptive technological
innovation, but in fact there is no true technical innovation in Bitcoin and blockchain. All the
components had already been developed before the publication of the Bitcoin whitepaper in 2008.
From a historical perspective, the technology has its roots in Ralph C. Merkle’s elaborations, who
proposed the Merkle Tree—the use of concatenated hashes in a tree for digital signatures—in the
1970s. Hashing has been used since the 1950s for cryptography for information security, digital
signatures, and message-integrity veriﬁcation. A decade later, Leslie Lamport proposed using the
hash chain for a secure login. The ﬁrst cryptocurrency for electronic cash was described at the
dawn of the web in 1990. Further evolutions and reﬁnements of the hash chain concept were
introduced in a paper by Neil Haller on the S/KEY application of a hash chain for Unix login,
P. Tasca (email@example.com) is founder and Executive Director of the Centre for Blockchain Technologies at the University College
‡C. J. Tessone (firstname.lastname@example.org) is Assistant Professor of Network Science at the University of Zurich and
co-founder of the UZH Blockchain Centre.
LEDGER VOL 4 (2019) 1-39
in 1994. While Adam Back proposed hashcash in 2002, the ﬁrst eletronic currency based on
a blockchain with the Proof-of-Work (PoW) concept was proposed in Nakamoto’s disruptive
While blockchains are still in their emergent technological phase, they are fast evolving
with the potential to see applications in many sectors of our socio-economic systems. According
to some statistics summarised by the World Economic Forum, the interest in blockchains has
Almost thirty countries are currently investing in blockchain projects. In
the ﬁnance sector, 80% of the banks predict to initiate blockchain-related projects by 2017.
Additionally, venture capital investments with a focus on blockchain activities grew to over 1.4
billion USD from 2014 to 2016. Since blockchain-based digital currencies combine together
features of money with those of a payment system,
central banks have also started to look into
the technology. Currently, over nineteen central banks worldwide do blockchain research and
development. Some of them—e.g., the Bank of England—have already commissioned studies on
CBDCs (Central Bank Digital Currencies). From the industry side, over one hundred corporations
have joined blockchain working groups or consortia and the number of patents ﬁlled has risen
to more than three thousand at the time of writing. These ﬁgures show the importance and
awareness of blockchain as one of the most promising emerging technologies that—together with
Artiﬁcial Intelligence, Internet of Things, and nanotechnology—will have a pervasive impact on
the future of our society.
1.2. Problem Statement and Research Method—At the time of writing, we may argue that—
according to the Technology Life Cycle theory—we are at the beginning of the so called phase
of “fermentation” which is characterised by technological uncertainty due to the evolution of
blockchains into alternative technological paths. The industry promotes different model designs
favouring functional and performance aspects over others in order to meet speciﬁc business goals.
At present, there are thousands of blockchain-based projects worldwide under development.
Some of them are simple forks of successful technologies, such as Bitcoin or Ethereum; others
propose completely new functionalities and architectures. It is for this reason that, instead of “the
Blockchain,” we refer to blockchains or blockchain technologies, that is, in order to encompass all
the possible architectural conﬁgurations and, for the sake of simplicity, also the larger family of
distributed ledger technologies (DLT) (i.e., community consensus-based distributed ledgers where
the storage of data is not based on chains of blocks). A heterogeneous development combined
with a lack of interoperability may endanger a wide and uniform adoption of blockchains in
our techno- and socio-economic systems. Moreover, the variations in blockchain designs and
their possible conﬁgurations represent a hindrance for software architectures and developers. In
fact, without the possibility to resort to a technical reference model, it is difﬁcult to measure
and compare the quality and performance of different blockchains and applications that sit atop
them. To summarise, current variations in blockchain software architectures pose a number of
concerns from different perspectives, speciﬁcally when it comes to heterogeneity. Heterogeneity
is a problem for the future development of blockchain technologies, because it will prevent their
development, adoption, and stimulation of innovation. Just a few of the problems it may create
reventing consistency in the drafting of laws and policies related to the regulation of
(2) Increasing ambiguity in the application of consumer protection laws and regulations.
2ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
ecreasing the predictability of how the workforce may be affected by blockchains/DLT.
ecreasing the accuracy of academic research into the concepts that underpin the devel-
opment of new applications and solutions.
indering the development and use of solutions using blockchains and DLT for ISO, IEC,
and other SDOs.
ncreasing the complexity of understanding the uses of blockchains/DLT for NGOs and
how these technologies may be applied in the relevant sectors to achieve social and
The solution to these problems is the setting up of software reference architectures where
standardised structures and respective elements and relations can provide templates for con-
crete blockchain architectures. Standards can emerge naturally because of market adoption
(industry-driven) or can be imposed by institutes and organisations. In the ﬁrst group we may
include initiatives like the Accord Project (https://www.accordproject.org/), the ChinaLedger
(http://www.chinaledger.com/) or R3 (https://www.r3.com/). In the second group we may refer
to the initiative conducted by the International Organization for Standardization (ISO) with the
establishment of the technical committee ISO/TC 307 on Blockchain and Distributed Ledger
Technologies. Several working groups have been set up with different topics to discuss. For just
one example, the ISO/TC 307/WG1 working group is engaged with reference architecture, taxon-
omy, and ontology. Overall, a long-term standardisation of blockchain reference architecture will
beneﬁt every industry. A standard for software reference architecture is necessary in order to
enable a level playing ﬁeld where every industry player and community member can design and
adopt blockchain-enabled products or services under the same conditions with possibility of data
exchange. As it is for the Internet, several institutes of standardisations (e.g., ETF in cooperation
with the W3C, ISO/IEC, ITU) set a body of standards. Internet standards promote interoper-
ability of systems on the Internet by deﬁning precise protocols, message formats, schemas, and
languages. As a result, different hardware and software can seamlessly interact and work together.
Applied to the World Wide Web (as a layer on the top of the Internet), standards bring interoper-
ability, accessibility and usability of web pages. Similarly, the adoption of blockchain standards
will promote the blossoming and proliferation of interoperable blockchain-enabled applications.
Thus, if we envisage a future in which blockchains will be one of the pillars of our societies’ de-
velopment, it is necessary to begin discussing and identifying standards for blockchain reference
architectures. The aim of this study is to highlight the need for standard technical reference mod-
els of blockchain architectures. This is conveniently aligned with the industry sentiment which
currently pushes organisations for standardisation to set industry standards. In order to support
an appropriate co-regulatory framework for blockchain-related industries, a multi-party approach
is necessary as it is for the Internet where both national standards, international standards, and a
mixture of standards and regulation are in place. In the mid-to-long term, the lack of standards
could bring risks related to privacy, security, governance, interoperability, and risk to users and
market participants, which could appear in the form of blockchain-related cyber crimes. From a
preliminary survey conducted in 2016 by Standards Australia, more than 88% of respondents
supported a role for standards in supporting the roll out of blockchain technologies.
above problems, the goal of this research is to conduct a review of the blockchain literature. This
will be a preparatory work in order to identify and logically group different blockchain (main
and sub) components and their layouts. In order to achieve our goal we propose a blockchain
3ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
taxonomy. Taxonomy comes from the term “taxon” which means a group of organisms. In
our case, taxonomy encompasses the identiﬁcation, description, nomenclature, and hierarchical
classiﬁcation of blockchain components. This is different from an ontology which would be
more focused on the study of the types, properties, and interrelationships of the components and
events that characterize a blockchain system. The methodological approach is composed of the
nalysis across blockchains. A precondition is the analysis of vocabulary and terms to sort
out ambiguities and disagreements. A literature review of the existing technologies is the
starting point to limit complexity and organise information in schematic order. To avoid
ambiguities the analysis is supported by a merge of common blockchain terminologies
developed so far in the literature and grouped in an online database.
This brings together
a vocabulary of key blockchain terms to provide readers with a foundation upon which
to understand the classiﬁcation and taxonomy developed in the rest of the analysis. The
identiﬁcation of the blockchain components is a crucial part of this analysis. In order
to explore all the possible domains of blockchain components and their topological
layout indicating their runtime interrelationships, we conduct a comparative study across
different families of blockchain applications: digital currencies, application stacks, asset
registry technologies, and asset-centric technologies. See Table 1 in Appendix and Tasca,
“Digital Currencies” (2015) for more information.6
ramework setting. After the comparative study, a hierarchical taxonomy (a tree structure
of classiﬁcations for a given set of components) has been deﬁned and populated with
main,sub and (when necessary) sub-sub components.
ayout categorisation. Finally, for the components in the lowest level of the hier-
archical structure, different layouts are introduced and compared. However, as the
technology keeps evolving, the layouts are increasing over time. Thus, for the sake of sim-
plicity, we limit our study to two or three main layouts per each sub or sub-sub component.
1.3. Results—The result of the component-level analysis is a universal blockchain taxonomy
tree that groups the major components in a hierarchical structure, and identiﬁes their functional
relation and possible design patterns.
In general, it is difﬁcult to evaluate whether a taxonomy or an ontology is good or bad,
especially if the domain is a moving target like blockchains. Taxonomies and ontologies are
generally developed to limit complexity and organise information, but all serve different purposes
and generally evolve over time (see e.g., the evolution of the famous Linnaean taxonomy in
biology). Thus, our taxonomy simply aims to contribute to set the foundations for classifying
different kinds of blockchain components. Without claiming to represent the ultimate structure,
the proposed taxonomy could be of practical importance in many cases. For example, it can:
upport software architectures to explore different system designs and to evaluate and
compare different design options;
e propaedeutic to the development of blockchain standards with the aim to increase the
adoption at a large scale of blockchain-enabled solutions and services;
nable research into architectural frameworks for blockchain-based systems in order
to boost the adoption of blockhain-enabled systems, through their interoperability and
4ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
(4) create gateway models to multiple blockchains and design governance frameworks;
(5) promote blockchain predictability;
id the development of regulatory frameworks that could provide a mix of both legal and
technical rules (i.e., regetech for blockchain-based systems).7
2. Background on Blockchain Technologies
Since Bitcoin’s inception in 2009, many blockchain software architectures have been deployed to
meet different technical, business, and legal design options. Given the current complex dynamic
of blockchain architectural development, it would be neither exhaustive nor comprehensive to
provide a picture of the existing blockchain technologies developed so far. Therefore, we take a
bird’s-eye view and describe blockchains by looking at their key driving principles, such as data
decentralisation, transparency, security, immutability, and privacy.8
Decentralisation of consensus
. The distributed nature of the network requires untrusted
participants to reach a consensus. In a blockchain, consensus can be based on “rules” (that
determine, e.g., which transactions are allowed and which are not, the amount of bitcoins
included in the block reward, the mining difﬁculty, etc.) or on the history of “transactions” (which
allows users to determine who owns what). The decentralised consensus on transactions governs
the update of the ledger by transferring the responsibilities to local nodes which independently
verify the transactions and add them to the most cumulative computation throughput (i.e., the
“longest chain” rule). There is no integration point or central authority required to approve
transactions and set rules. No single point of trust and no single point of failure.
. Records are auditable by a predeﬁned set of participants, although the set can
be more or less open. For example, in public blockchains everyone with an Internet connection to
the network holds equal rights and ability to access the ledger. The records are thus transparent
and traceable. Moreover, network participants can exercise their individual (weighted) rights
(e.g., measured in CPU computing power) to update the ledger. Participants have also the option
to pool together their individual weighted rights.
. Blockchains are shared, tamper-proof, replicated ledgers where records are
irreversible and cannot be forged thanks to one-way cryptographic hash functions. Although
security is a relative concept, we can say that blockchains are relatively secure because users
can transfer data only if they posses a private key. Private keys are used to generate a signature
for each blockchain transaction a user sends out. This signature is used to conﬁrm that the
transaction has come from the user, and also to prevent the transaction from being altered by
anyone once it has been issued.
. Blockchains function under the principle of the non-repudiation and
irreversibility of records. Blockchains are immutable because once data has been recorded in the
ledger, it cannot be secretly altered ex-post without letting the network know it (i.e., their data
is tamper-resistant). In the blockchain context immutability is preserved thanks to the use of
hashes (a type of a mathematical function which turns any type of input data into a ﬁngerprint of
5ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
ﬁxed size, that data called a hash. If the input data changes even slightly, the hash changes in
an unpredictable way) and often of blocks. Each block includes the previous block’s hash as
part of its data, creating a chain of blocks. The strength of a given blockchain’s immutability is
relative, and relates to how hard the history of transactions is to change. Indeed, it becomes
very difﬁcult for an individual or any group of individuals to tamper with the ledger, unless
these individuals control the majority of “voters.” For public proof-of-work blockchains such as
Bitcoin, the immutability is related to the cost of implementing a so-called “51% attack.” For
private blockchains, the block-adding mechanism tends to be a little different, and instead of
relying on expensive Proof-of-Work, the blockchain is only valid and accepted if the blocks
are signed by a deﬁned set of participants. This means that, in order to recreate the chain, one
would need to know private keys from the other block-adders. A complete discussion of threats
to the immutability of a given transaction history can be found in Barber (et al.) “Bitter to
While blockchains are technically immutable, from a governance perspective,
this immutability is never fully realised. There have been several examples where the Bitcoin
community has reverted Bitcoin blocks based on community decisions. The division between
Ethereum and Ethereum classic, and later between Bitcoin and Bitcoin Cash and Bitcoin Gold
are not purely anecdotal evidence: they are strong indicators of the importance that governing
bodies—even if informal—end up having on the information eventually stored in a blockchain.
Other non-fundamental properties of blockchains include data automation and data storage
Automation and smart contracts
. Without the need for human interaction, veriﬁcation, or
arbitration, the software is written so that conﬂicting or double transactions are not permanently
written in the blockchain. Any conﬂict is automatically reconciled and each valid transaction
is added only once (no double entries). Moreover, automation regards also the development
and deployment of smart legal contracts (or smart contract codes, see Clack, Bakshi, and
Braine, “Smart Contract Templates” (2016)
) with payoff depending on algorithms which are
self-executable, self-enforceable, self-veriﬁable, and self-constrained.
. The storage space available on blockchain networks can be used for the storage
and exchange of arbitrary data structures. The storage of the data can have some size limitations
placed to avoid the “blockchain bloat” problem.
For example, metadata can be used to issue
meta-coins: second-layer systems that exploit the portability of the underlying coin used only as
“fuel.” Any transaction in the second layer represents a transaction in the underlying network.
Alternatively, the storage of additional data can occur “off-chain” via a private cloud on the
client’s infrastructure or on public (P2P or third-party) storage. Some blockchains, like Ethereum,
also allow the storage of data as a variable of smart contracts or as a smart contract log event.
3. Taxonomy of Blockchains
The diversity of blockchain research and development provides an opportunity for cross-
fertilisation of ideas and creativity, but it can also result in fragmentation of the ﬁeld and
duplication of efforts. One solution is to establish standardised architectures to map the ﬁeld
6ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
and promote coordinated research and development initiatives. However, in terms of blockchain
software architecture design, little has been proposed so far,
and the problem of consistently
engineering large, complex blockchain systems remains largely unsolved. We approach this
problem by proposing a component-based blockchain taxonomy starting from a coarse-grained
connector–component analysis. The taxonomy compartmentalises the blockchain connectors/-
components and establishes the relationships between them in a hierarchical manner. We adopt a
reverse-engineering approach to unbundle the blockchains and divide them into main (coarse-
grained) components. Each main component is then split into more (ﬁne-grained) sub and
sub-sub components (where necessary). For each of these sub (and/or sub-sub) components,
different layouts (models) are identiﬁed and compared. By deriving the logical relation between
(main, sub or sub-sub) components, the study helps to clarify the alternative modus operandi of
the blockchains, and helps to develop the conceptual blockchain design and modelling.
Similarly to other ﬁelds, like electronics or mechanics,
the software engineering approach
used to derive the taxonomy treats blockchains as the result of gluing together prefabricated,
well-deﬁned, yet interdependent components. Although equivalent components provide similar
services and functions, they can be of different importance and type, and the interconnection may
work in different ways. Following this logic, each of the next seven sections will introduce a new
blockchain main component and its sub (and eventually sub-sub) components by describing and
comparing their layouts.
The ﬁrst identiﬁed main component is Consensus. It relates to the set of rules and mechanics
that allows for the maintainance and updating of the ledger and guarantees the trustworthiness
of the records in it, i.e., their reliability, authenticity, and accuracy.
Consensus varies across
different blockchain technologies, with every consensus mechanism bringing advantages and
disadvantages based on different characteristics e.g., transaction speed, energy efﬁciency, scala-
bility, censorship-resistence, and tamper resistance.
The set of rules and mechanics compose
the framework of the validation process that is necessary to overcome security issues during the
validation. Figure 1 illustrates the subcomponents forming the component Consensus:
• Consensus Network Topology
• Consensus Immutability and Failure Tolerance
• Consensus Agreement
Those subcomponents and sub-subcomponents must be jointly considered when designing an
active network consensus validation process, because not only their individual conﬁguration but
also their combination determine when and how the overall blockchain agreement is achieved
and the ledger updated.
4.1. Consensus Network Topology—Consensus Network Topology describes the type of
interconnection between the nodes and the type of information ﬂow between them for transaction
and/or for the purpose of validation. For efﬁciency reasons, systems have historically been
7ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
Consensus Network topology
Consensus Immutability &
in the Blockheader
Binary Merkle Tree
Patricia Merkle Tree
Full Node Only
Thin Node Capable
Block Storage Storage
Limits to Scalability
Number of Transactions
Number of Nodes
Number of Users
Block Confirmation Time
Convertible Multiple Asset
No Tokenisation Present
Tokenisation+Third party Add-Ons
Generic Non-Turing Complete
Application specific Non-Turing Complete
Non-Turing Complete + External Data
Security and Privacy
Built-in Data Privacy
Add-on Data Privacy
Permissioned Public Blockchain
Permissioned Private Blockchain
Block + Security Reward
Blockchain Ontology Overall Matrix
Fig. 1. Blockchain Taxonomy Tree: A representation of the taxonomic deconstruction of
8ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
designed in a centralised manner. This centralisation lowers dramatically the costs for system
conﬁguration, maintenance, and adjustment (and the costs of arbitration in case of conﬂict) as this
work has to be performed only once in a central place. While highly efﬁcient in many situations,
this kind of system creates a single (or very limited set of) point(s) of failure and suffers from
scalability issues. With respect to the network topology, this hierarchical arrangement is still
present in most of our techno- and socio-economic systems (one example is the modern electronic
payment system). To avoid the single point of failure, these centralised systems can be extended
into hierarchical constructs which exhibit larger scalability and more redundancy, while keeping
the communication efﬁcient. Decentralised solutions have been proposed as a alternatives to
those centralised topologies. Since the dawn of the Internet, technical systems have evinced a
transition towards decentralised arrangements where all the nodes are equivalent to any other.
For most applications, blockchain-based systems—with their federated sets of participants—are
a clear example of this kind. Blockchain-based systems resort to speciﬁc topologies to create the
peer network that ultimately determines how the validation process will evolve.
It is important to mention that Consensus Network Topology is linked to the level of
(de)centralisation in the validation process, but this is not the only determinant. Also, other
factors like the reward mechanism (see Section 11) heavily inﬂuence the validation.
As a matter
of an example, Bitcoin has a decentralised validation process, which is still accompanied by an
ever-increasing concentration of computational power devoted to the Proof-of-Work by network
participants. Indeed, during the period 2013-2015, the cumulative market share of the largest ten
pools relative to the total market hovered in the 70% to 80% range.6
We identify three possible layouts for Consensus Network Topology:
There exist implementations that are decentralised. Bitcoin as the pio-
neer in digital currencies established a distributed P2P network, which enables direct
transactions between every node within the network. The validation process within the
Bitcoin network is decentralised through miners and full nodes who validate the transac-
tions within the network connected in a random way, as provided by super-nodes.1This
network illustrates a decentralised Consensus Network Topology. Obviously, this layout
is independent from the Consensus Immutability layout (Peercoin and NXT also show
decentralised network topologies).
There are other implementations that are not decentralised, and there exists
differencces in the roles nodes have. For example, in Ripple the network topology is
divided into tracking (or stock) and validating nodes.
The tracking nodes are the gateway
for submitting a transaction or executing queries for the ledger. They make also available
the transactions to the broader network via history sharding.
In addition to that the
validating nodes have the same functions as tracking nodes, but they can also contribute
additional sequences to the ledger by validation, vote on fees and amendments.
Kind of solution yields (or can be extrapolated to) a hierarchical network topology. In the
community of developers these hierarchical topologies are also referred to as “Consortium
In some speciﬁc implementations, a central authority may need (or wish)
to control what is added to the ledger. An example for this are digital versions of
ﬁat currencies: the so-called Central Bank Digital Currencies (SBDCs). This kind of
solution yields a third layer, “Centralised topology,” which is intimately related to private
9ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
blockchains. It is important to mention that a centralised solution would normally be
considered a non-properly working design (or a non-solution) if implemented in terms of
a blockchain, as it would have been implemented in a more transparent manner otherwise.
Normally, some level of federation and redundancy are key to blockchain systems.
4.2. Consensus Immutability and Failure Tolerance—In general, the failure tolerance of
a distributed system should be deﬁned with respect to three interrelated issues: faults (e.g.,
Byzantine faults), errors, and failures. See e.g., Driscoll (et al.) “Byzantine Fault Tolerance”
(2003) and Castro (et al.) “Practical Byzantine Fault Tolerance” (1999) for Byzantine fault
tolerance in distributed systems.
There are different types of failures, and generally it is
costly to implement a fault-tolerant system. Practically, it is not possible to devise an infallible,
reliable system. For a literature review and deeper analysis of fault-tolerant distributed systems,
we refer the reader to Cristian, “Understanding Fault-Tolerant Distributed Systems” (1991) and
Fischer, “The Consensus Problem in Unreliable Distributed Systems” (1983).
as a special kind of distributed system, is fault-tolerant when it shows the ability to continue
functioning: i.e., it must grant reliability, validity, and security of the information stored in the
ledger. Indeed, blockchains represent a decentralised solution to the problem of information
storage which requires no central database but rather many duplicates, such that each server
holds a replica of the ledger. Any new record is costly (often measured in terms of computational
power) to add to the ledger, but cheap to have veriﬁed by peers. Therefore, a blockchain system
is in need of an efﬁcient consensus mechanism to ensure that every node has its original version
of the full transaction history which is kept consistent with the other peers over time. In this
regard, the immutability of achieved consensus differs with respect to the resources required to
maintain large network security. In the past years, the evolution of blockchain technologies has
been accompanied by the development of different mechanisms that help to keep the information
contained in the ledger reliable, valid, and secure. The mechanisms for Consensus Immutability,
together with the subcomponents of Consensus Agreement, determine the failure tolerance of
a blockchain. As of the time of writing, we have identiﬁed six main layouts for Consensus
Immutability and Failure Tolerance:
. The most widely used cryptocurrency, Bitcoin, uses Proof-of-Work
(PoW) to ensure the immutability of transaction records. In this setup, computing devices,
usually called miners, connected to a peer-to-peer network, perform the task of validat-
ing the transactions proposed for addition to the complete record of existing—valid—
transactions. The generation of a block that can be appended to the blockchain—rendering
in this way valid all transactions included therein—requires solving the inversion of a
cryptographic function, which can only be done by brute force. In PoW, the probability
that a miner mines a new block depends on the ratio between the computational power
he devotes to this task and the total instantaneous computational power by all miners
connected to the network. Speciﬁcally, miners must ﬁnd a solution to a one-way hash
function by computing new hash values based on the combination of the previous hash
values contained in the message, the new transactions in the block they create, and a
nonce. The solution is such that the new hash value will start with a given number of
target. As of the time of writing, the mining process needs several requirements
to be successful.
These include specialised hardware, which is needed to perform the
computational tasks, and ever-increasing amounts of electricity to power the hardware.
10 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
These computations are run by dedicated machines (ASICs) which are very expensive as
well as resource-intensive, contributing to a large electricity footprint for cryptocurrency
Due to this scheme, in the last years miners have agglomerated around mining
A clear drawback of the PoW mechanism, then, is that the inherent inefﬁciency
in resource requirements, and the large-scale investments needed, have led to long-term
centralisation of the mining power. In late 2017, almost ﬁve quadrillion SHA256 com-
putations were performed each second in the Bitcoin mining process. Regretfully, these
computations do not have any practical or scientiﬁc relevance apart from ensuring that the
process of block creation is costly, while maintaining that the process of block veriﬁcation
remains simple for peers.
Interestingly, when two adversaries coordinate, it is sufﬁcient that each hold only around
25% of the total computing power to mount an attack.
In this layout, there exists the
risk of monopoly mining, induced by large coordination of miners in a single mining
pool, which continuously increases the expected payoff of others if they join said mining
pool. In this hypothetical situation, said mining pool can censor speciﬁc transactions and
dictate which transactions are accepted and which not.
In contrast, BFT consensus mechanisms tolerate at most
corrupted nodes in the
asynchronous communication protocol and even higher levels in the synchronicity case.
Electricity consumption can be estimated around 0.1 to 1 W/GH corresponding to around
1GW of electricity consumed every second. For this reason, other developers within the
area of blockchain technologies are continuously attempting to develop novel mechanisms
to achieve an equivalent result. It is worth mentioning that some cryptocurrencies (e.g.,
Primecoin) have tried to make the PoW a task that serves a useful aim (in said case,
searching for long chains of prime numbers, or Cunningham chains).
. PoS links block generation to the proof of ownership of a certain amount
of digital assets (e.g., digital currencies) linked to the blockchain. The probability that a
given prover is selected to verify the next block grows in relation to the share of assets that
prover has within the system. The underlying assumption is that users with a large share
of the system wealth are more likely to provide trustworthy information with respect to
the veriﬁcation process, and are therefore to be considered a more trustworthy validator.
Two alternative PoS methods have been devised. The ﬁrst one is based on randomised
block selection (used in e.g., NXT and BlackCoin); it uses a calculation searching for
the lowest hash together with the stake size; it is therefore somewhat deterministic and
each node can independently determine the likelihood of being selected in a future
round. An alternative scheme is the coin-age-based selection (used by e.g., Peercoin,
being actually the ﬁrst one to be implemented in real world conditions) which combines
randomisation with coin-age (a number derived from multiplying the amount of assets
held by the prover and the length of time those assets have been held). Although PoS
has the chance to solve two issues with PoW (risk of monopoly mining and resources
wasted in the mining process), it is affected by the “nothing at stake” issue. Because there
is little cost in working on several chains (unlike in PoW), one could abuse the system by
voting for multiple blockchain-histories which would prevent the consensus from ever
resolving (double spending). This problem can be addressed by Delegated Proof-of-Stake
(DPoS), a generic term describing an evolution of the basic PoS consensus (utilised
11 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
in, e.g., BitShares, Casper by Ethereum, and Tendermint) where blocks are forged by
predetermined users delegated by the user who has the actual stake. These forgers are
rewarded for their duty and are punished for malicious behaviour (such as participation in
double-spending attacks). This principle of pre-authorised forgers is generalised by the
. In this case, participants are not asked to solve arbitrarily difﬁcult
mathematical problems like in PoW, but instead are asked to use a hard-conﬁgured set of
“authorities” empowered to collaborate “trustlessly.” Namely, some nodes are exclusively
allowed to create new blocks and secure the blockchain. Typically, Proof-of-Authority
(PoA) mechanisms ﬁt well for consortium private networks where some preselected real
entities (i.e., the authorities) are allowed to control the content that is added to the public
registry. Those nodes will receive a set of private keys that will be used to “sign” the new
blocks, acting as trusted signers. Thus, every block (or header) that a client sees can be
matched against the list of trusted signers. The challenges brought by PoA are related
to: control of mining frequency, distribution of mining load (and opportunity) between
the various signers, and maintenance of the the list of signers so as to be robust from
malicious attacks even in presence of dynamic mutation of the trusted signers.
(4) Proof-of-Capacity/Proof-of-Space and Proof-of-Storage
. PoC or PoSpace and PoS-
torage are implementations of the popular idea of “space as resource.” Here the focus is
not on the CPU cycles but on the amount of actual memory (non-volatile) space the prover
must employ to compute the proof. Nodes are asked to allocate a signiﬁcant volume of
their hard drive space to mining instead of using CPU-bound space as in PoW. Miners
are incentivised to devote hard-drive capacity as those who dedicate more disk space
have a proportionally higher expectation of successfully mining a block and reaping the
reward. The PoC makes use of hash trees to efﬁciently allow veriﬁcation of a challenge
without storing the tree. These schemes are more fair and green than PoW. The reason
mainly comes from the lower variance of memory access times between machines and
the lower energy cost achieved through the reduced number of computations required.
Several practical implementations adopt the PoC consensus algorithm like Permacoin,
SpaceMint, and Burstcoin, to cite just a few. PoC consists of an initialisation and subse-
quent execution between a prover
and a veriﬁer
some amount of work has been completed,
that she has allocated some
number of bytes of storage. After the initialisation phase,
is supposed to store some
only holds some small piece of information. At any later
point in time
can initialise a proof execution phase, and at the end
outputs reject or
accept. The PoC is in general deﬁned by three quantities:
; then, the miner
shows that she either: 1) had access to at least
storage between the initialisation and
execution phases and at least
space during the execution phase; or 2) used more than
time during the execution phase. Solutions to the “mining multiple chains” and “grinding
blocks” problems of PoC algorithms have been proposed by Park (et al.) “Spacecoin”
(2015), among others.
The Proof-of-Storage (PoS) mechanism is similar to PoC but the
designated space in it is used by all participants as common cloud storage.31
. In Proof-of-Burn (PoB), miners must prove that they burned some digital
assets. They do so by sending them (e.g., digital currencies) to a veriﬁable unspendable
12 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
address belonging to them. Similarly to the PoS, PoB is intended to minimise the waste of
resources generated by PoW. However, at the current stage, all PoB mechanisms function
by burning PoW-mined digital currencies. This is therefore an expensive activity as the
digital currencies once required to work as “fuel” in a PoB system cannot be recovered.
PoB can be used also to bootstrap a token off of another (see e.g., Counterparty or
. The more advanced hybrid consensus immutability and failure tolerance methods
are “PoB and PoS,” where Proof-of-Burn blocks act as checkpoints, and “PoW and PoS,”
where PoW blocks act as checkpoints containing no transactions, but anchor both to each
other and to the PoS chain. Peercoin uses PoW/PoS consensus. To solve the “nothing at
stake” issue, Peercoin uses centrally-broadcast checkpoints (signed under the developer’s
private key) according to which no blockchain reorganisation is allowed deeper than
the last known checkpoints. Here the problem is that the developer becomes the central
authority controlling the blockchain.
4.3. Gossiping—Blockchains are also decentralised, redundant storage systems. This
redundancy makes it very difﬁcult to hijack the information stored in them. How this information
travels through the network of computers is a characteristic that varies from one blockchain
system to another. Given the lack of a central routing authority (like it would exist, for example,
in traditional electronic payment systems) nodes must transmit the information they possess—in
general new blocks, but it may be also the full blockchain to new nodes that enter the network—to
peers they know are participating in the system. To this aim, nodes possess a list of peer nodes.
Whenever a new block is added to the local blockchain of a node, it passes the block to others in
its peer list by Gossiping.
We identify two possible layouts for Gossiping:
Gossiping occurs ﬁrst in a local manner (through a local validation process) until
consensus is reached. This is also called “federated consensus” used e.g., in Ripple, in
which nodes can share transaction records to another node and reach consensus without
directly knowing all the nodes in the network.
Therefore most information travels
“locally”—in terms of the P2P network—such that a consensus is reached at this initial
level. Only then is the information sent throughout all the other nodes. In this layout, the
Gossiping can be termed “local.”
In most implementations—Bitcoin, Ethereum, etc.—Gossiping occurs to a list of
peers that have been selected by what in the Bitcoin network are called fallback nodes.
These fallback nodes maintain a list of all peers in the network. Upon connection of a new
node, they submit a randomly chosen list of peers to the entrant one. The logical network
topology is intended to be largely unstructured, similar to the Erdos-Renyi network.
Such topology lacks a concept of vicinity or local neighbourhood, and therefore the
Gossiping process can be termed global.
4.4. Consensus Agreement—Aconsensus agreement deﬁnes the set of rules under which
records (like sets of transactions or any other atomic piece of information) are independently
updated by the nodes of a distributed system. This is important to understand how a distributed
system is able to handle the so-called Byzantine failures, i.e., how the system composed of
nodes can achieve consensus on storing veriﬁed, trustworthy, information even in the presence
malicious nodes or in presence of malicious participants launching sybil attacks.
13 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
regard, it is very important to understand how the nodes communicate between them.
Latency is a sub-subcomponent which describes the rule of message propagation in the networks.
(1) Synchronous Communication.
Systems which set upper bounds on “process speed
interval” and “communication delay” such that every message arrives within a certain
known, predeﬁned, time-interval (
) are called synchronous. This does not preclude the
possibility of having message delays due to exogenous network latency, but the delay
is bounded and any message that takes longer than
is discarded. Lax synchronicity
assumptions apply also to the Bitcoin blockchain. For example, a block is rejected if
contains a timestamp: 1) lower than (or equal to) the median timestamp of the previous
eleven blocks; and 2) greater than (or equal to) the “network-adjusted time” plus 2 hours.
Another example of blockchain adopting synchronous communication is Ripple, through
the use of clocks. Speciﬁcally, Ripple’s “LastLedgerSequence” parameter asserts that a
transaction is either validated or rejected within a matter of seconds.
(2) Asynchronous Communication.
Systems which do not set any bound on “process speed
interval” and “communication delay,” such that every message/packet can take an in-
deﬁnite time to arrive, are called asynchronous. Although this type of communication
protocols brings some advantages (e.g., calls/requests do not need to be addressed to
active nodes and nodes do not need to be available when a new information is sent to
them by peers), its main disadvantage is that response times are unpredictable and it is
harder to design applications based on them. Synereo is an example of a blockchain using
an asynchronous communication protocol.
Finality describes whether information intended to be stored in a blockchain (or, as a matter of
fact, in any system) can be safely considered perpetually stored once the recording is performed.
For a distributed system like blockchain-based ones this is very challenging to achieve, and it is
certainly not one of the underlying design principles. In a system where the new blocks diffuse
through gossiping, and because of rules such as the precedence of the longest chains, even if
consensus is achieved globally, a priori nothing prevents a set of new nodes entering the system
and overriding the previous consensus by offering a longer versions of the history. We identify
two possible layouts for Finality:
. In this case, the consensus agreement “eventually settles.” Non-
deterministic systems use randomised or inherently probabilistic consensus (also called
stabilising consensus) in which the probability to disagree decreases over time. For
example in the Bitcoin blockchain the block frequency is adjusted (with respect to the
block-mining rate and indirectly to the computational power of the nodes) to minimise
the probability of forks. Moreover, the propagation of blocks through the network has
characteristic delays and even in the presence of only honest nodes the fork probability
cannot be ruled out simply because different nodes may ﬁnd competing blocks of the
same height before the one found ﬁrst reaches the complete network.
be prevented even if there is in place a concurrency control mechanism, which which
14 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
attempts to correct results for simultaneous operations. Therefore, overall, the protocol is
non-deterministic. Thus, even though the widespread heuristic “wait until 6 conﬁrmed
blocks are appended to the chain” reduces the likelihood that a transaction is overridden
afterwards, it does not eliminate completely the probability of a previously validated
block being pruned and removed from the blockchain in the future.
. In this case, consensus agreement converges with certainty and transac-
tions are immediately conﬁrmed/rejected in/from the blockchain. This property turns out
to be very useful for smart contracts where, using state-machine replication, consistent
execution of the contracts can be achieved across multiple nodes. All blockchains based
on Lamport Byzantine Fault Tolerance achieve deterministic consensus.
A prime exam-
ple of an implementation featuring deterministic ﬁnality is Stellar. Another example of
deterministic systems is in private blockchains, where new blocks follow a predeﬁned set
5. Transaction Capabilities
The second main component, Transaction Capabilities, is important to illustrate scalability of
transactions and usability in possible applications and platforms. One of the major challenges
for blockchain technologies is to increase the transaction throughput to compete with other
solutions already available in the market (e.g., centralised payment systems, like credit cards). In
order to achieve these improvements, quantitative parameters (e.g., data storage in block header,
transactions per second (or TPS)), need to be redesigned to realise such improvements. Figure 1
illustrates the subcomponents forming the component Transaction Capabilities:
• Data Structure in the Blockheader
• Transaction Model
• Server Storage
• Block Storage
• Limits to Scalability
–[5.4] Conﬁrmation Time
5.1. Data Structure in the Blockheader—The data stored in the block header has different
functions. On the one hand, it includes the transaction hashes for validation purposes; on the
other, it contains additional information for different application layers or blockchain technology
platforms. The data structure in the blockheader describes the capabilities of the system to store
transaction information. The original application of Merkle proof was implemented in Bitcoin,
as described in Nakamoto’s white paper.1We identify two possible layouts for data structure in
(1) Binary Merkle Tree.
Bitcoin uses the binary Merkle tree within the block header to
store the transactions.
The information in the block header in the Merkle tree structure
contains a hash of the previous header, timestamp, mining difﬁculty value, proof of work
nonce, and root hash for the Merkle tree containing the transactions for that block, which
are used for the veriﬁcation process to scale up the transactions speed. By convention, the
15 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
longest chain (from the so-called Genesis block) is considered to be the current status of
(2) Patricia Merkle Tree.
One the one hand, the Patricia Merkle tree or simply Patricia
allows activities like inserting, editing or deleting information referring to the balance
and nonce of accounts, which enables faster and more ﬂexible validation of transactions
than the binary Merkle tree.
However, with respect to applications, it has the important
advantage of allowing for veriﬁcation of speciﬁc branches of the tree. Ethereum uses the
Patricia tree within the block header to store more information than what is possible in
the binary Merkle tree.
Those contain transactions, receipts (essentially, pieces of data
showing the effect of each transaction), and state.
Importantly, this technology allows
even blocks outside the longest chain to contribute to the validation process, building a
conﬁrmation system that is less centralised. This is the so called Ghost rule, a variant of
which is also implemented in the Ethereum blockchain.39
5.2. Transaction Model—The transaction model can be imagined as an accounting ledger
which tracks the inputs and outputs of each transaction. The transaction model describes how
the nodes connected to the P2P network store and update the user information in the distributed
ledger. The challenge of the transaction model is to prevent data that ought not to be trusted
by the parties connected to the system—e.g., data originating in untrustworthy behaviour, like
double spending—from entering into the ledger
As of the time of writing, it is possible to identify two possible transaction model layouts in
the more widely used blockchain-based systems:
(1) The Unspent Transaction Output (UTXO).
The UTXO model includes a refractory
number of blocks (currently 100 for Bitcoin ) during which network participants are
prevented from using the transaction output in new transactions. In this way, it forbids
miners from spending transactions fees and block rewards before stable validation status
of the block chain. This measure prevents the forking problem of blockchains. This
transaction mechanism is available in blockchain technologies like Bitcoin.41
(2) Traditional Ledger.
In comparison to the UTXO model, different implementations of
blockchain systems—like Stellar and Ripple—use a more traditional ledger model to
record the transactions recorded in the system. In particular, Stellar lists every single
transaction in the Stellar distributed ledger history. Also, Ripple uses the traditional ledger
transaction model to register increments/decrements of balance and clear all account
balances. In Ethereum some transactions are used to execute actions in smart contracts
deﬁned in speciﬁc atomic records in the blockchain. Those transactions can be seen as
order executions of stakeholders which perform the actions out of said smart contracts.
5.3. Server Storage—At the core of blockchain-based systems is their decentralised nature.
This requires that nodes connected to the peer-to-peer network are indistiguishable from each
other. This concept, however, cannot be fully realized when the storage needs, computing power,
or bandwidth constraints of the network nodes do not permit it. In these scenarios, different
nodes have access to different layers of information, and those which do not store the information
fully are “thin clients” connected to the peer-to-peer network.
We identify two possible layouts
for Server Storage:
(1) Full Node Only.
All nodes connected to the network, and which are part of the validation
16 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
process, are of the same kind. This is a genuinely peer-to-peer network where all the
nodes are equivalent in terms of information contained. This property creates a large
degree of information redundancy, which makes the system more resilient to attacks or
(2) Thin Node Capable.
In this setup, some nodes connected to the network contain only a
selected subset of all the information contained in the blockchain. This creates more scal-
able systems (in terms of number of nodes connected to the network and the concomitant
network trafﬁc and storage needs), but may deteriorate the resiliency as only a fraction of
the nodes contain the complete blockchain information.
5.4. Block Storage—The information stored in the blockchain determines the scalability
of the system across some dimensions. More crucially, it also allows us to understand how
concomitant information from users are abstracted within the system. We identify two possible
layouts for Block Storage:
In systems like Bitcoin, only the transactions are stored. They contain a
set of inputs and outputs that help to identify the emitter(s) and receiver(s) of a speciﬁc
transaction. This kind of approach is preserved in more exotic applications of blockchain-
like technologies, like IOTA, which relies on the storage of a directed-acyclic-graph to
store every single transaction. This kind of approach works not only for cryptocurrency
applications, but also underlies all property-transfer-like applications.
(2) User Balance.
In systems like Ripple, the decentralised storage also contains information
about user balances. This approach may limit the storage needs of the system, but at the
same time reduces accountability and the possibility to roll back transactions.
5.5. Limits to Scalability—The decentralised nature of blockchain systems and the con-
comitant redundancy in storage impose different kinds of limits to the way in which a speciﬁc
implementation scales with system size. System size is used here in a broad sense: it may refer
to the number of nodes connected to the network, the number of users of the service, the set
of network connections and/or amount of network trafﬁc, the number of transactions, etc. It
is worth remarking that these ingredients are intertwined in the real world,
upon usage and continuing development—the limiting factor of a particular blockchain system
may vary over time. Because blockchain technologies are rapidly evolving at present,
limits are often changed in the course of platform development. An example of a change in
scalability brought by technological evolution is the adoption of Segregated Witness (Segwit)
This technology (under the name BIP141) provided a solution to the problem
of transaction malleability and enabled the storage of more transactions per block. Although
some exponents of the Bitcoin community criticized Segwit to be a non-sufﬁcient short-term
it triggered interesting side consequences for the Bitcoin system: ﬁrst, the
implementation of micropayment channels where the transactions are not necessarily recorded in
the blockchain but take place off-chain between multiple trusted parties;
and second, the
proposal of SegWit2x, which would have enabled an alternative approach to store information in
the transactions, modifying the scalability properties of the platform by increasing the block size
to two megabytes.49
The importance of this component is due to its inﬂuence on the ﬁnal scaling of the system.
Scaling is a property that speciﬁes how growth will inﬂuence its overall performance. As an
example, how the total network trafﬁc induced by unveriﬁed transactions grow with the number
17 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
of network nodes. If every node has a small—limited—number of connections, then the total
network trafﬁc will scale linearly with the number of nodes. In mathematical terms it will be
. However, if every node is connected to each other, then the trafﬁc will be
will grow quadratically with the number of nodes. Therefore, if—for a given implementation—
network trafﬁc is the most crucial limiting factor, different logical topologies will have different
scalability. Acknowledging that a categorical deﬁnition is a crude simpliﬁcation, we will focus
here on the most limiting element for each system. We identify ﬁve possible layouts, categorized
by their respective Limits to Scalability:
(1) Number of Transactions.
The most common real-world example is found in Bitcoin,
which has a limitation in the number of transactions it can process in every block, because
of the hard-coded limit to the block size in bytes. Given that new blocks appear (on
average) every ten minutes, this means that the number of transactions that can be included
in a given time window is limited. Therefore the layout “Number of Transactions” refers—
regardless of the information stored in the blockchain—to the speciﬁc implementations,
where the number of operations that can be included in the blockchain is severely limited
(2) Number of Users.
Bitcoin only stores transactions in its public ledger. This is different
from other related technologies. Ripple, on the other hand, stores not only transactions,
but also the state of the Ripple accounts. Therefore, in scenarios like this, it is the number
of users of the system that limits its scalability. A similar problem occurs in Ethereum
where the system will be constrained by the number of DAOs, individuals, etc. that it
will contain, as these are the actors that generate activity in the system. Therefore, the
term “Number of Users” for this layout is a broad reference to the number of objects
with stored states. Needless to say, this layout is somewhat linked to the ﬁrst, in that the
number of transactions will depend on the number of users. However, each limiting factor
can still appear irrespective of the other.
(3) Number of Nodes.
The number of nodes connected to the network, acting as veriﬁers
for the information that is stored on the blockchain, presupposes a limiting factor because
of the mechanism of information diffusion adopted. Gossiping is a process that requires
larger times in decentralised networks to propagate into a consensus state,
even reach a point—when the relative time taken by network trafﬁc is very long—where
consensus cannot longer be reached and the blockchain naturally forks. Therefore this
process naturally limits the applicability of fully decentralised solutions.
(4) Possible Values.
Each of these three layouts can have different values, depending on how
detrimental a speciﬁc layout is to the overall performance of the system. The possible
values that each layout can have are divided into four: (i)
at most linear
at most quadratic
, and (iv)
worse than quadratic
. The ﬁrst value describes when
the relevant global characteristics of a system are independent of the number of speciﬁc
the other three categorical values depend on the number of elements in said
class. For example, the number of users is largely irrelevant to the performance of the
Bitcoin network (because this number is never translated into any property of the network).
However, the number of transactions is linearly related to local network trafﬁc.
(5) Conﬁrmation Time.
The time it takes a speciﬁc action to be conﬁrmed ultimately de-
pends on the time it takes for it to be added to the blockchain, and to be validated by
18 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
further blocks later appended to it. Different approaches can be taken to this process:
addition of new blocks at regular intervals (taken by Peercoin) and
addition like in Bitcoin, where the process of mining induces an exponential distribution
of inter-block discovery time.
6. Native Currency/Tokenisation
So far, cryptocurrencies and other transfer of property records are the most common usage
of blockchain technologies. In cryptocurrencies, system participants who contribute to the
veriﬁcation process—if selected by some rule to issue a new block into the blockchain—are
awarded the possibility to issue a transaction without issuer (so called “coinbase”) to themselves.
On the one hand, this is a customary way of introducing new assets into the system. On the other,
it introduces an incentive for users to participate of the veriﬁcation process which leads to an
increased trustworthiness on the system.
The aforementioned incentive scheme is to be provided in a token,
whose value is assigned
precisely because of the cost associated with its production.
Initially, solutions like Bitcoin
created their own (and single) asset class (the bitcoin) that can be used for transactions within
the system. This particular solution is not the only one possible—for example Ethereum,
where beyond the native token, Ether, arbitrary new tokens can be created and exchanged via
smart contracts. Further, the native currency possibilities (present, for example, in Ripple and
tokenisation) enable different use cases of blockchain technologies like asset-transfers via tokens,
All this is just the beginning of cryptoeconomics: it is of the utmost importance
how these assets are supplied to the system, because this affects the way users are incentivised to
participate in the validation process. Figure 1 (above, page 107) illustrates the subcomponents
forming the component Native Currency/Tokenisation:
• Native Asset
• Asset Supply Management
6.1. Native Asset—Some systems implemented using blockchain technologies have an
underlying native asset (which are normally called cryptocurrencies) which are digital tokens
whose owners assign a value and which allow for the daily activities on the platforms or in
their respective communities. Opinions may vary on whether these cryptocurrenties ought to be
considered ﬁat or commodity currencies,
and whether they may eventually be massively
adopted replacing traditional ones.59 We identify three possible layouts for native assets:
Private blockchain implementations do not require a native asset within to in-
centivise participation. In these cases, there is no native asset incorporated into the
(2) Own Cryptocurrency.
Most implementations of cryptocurrencies only deal with transfer
of property of its own tokens within the system. Bitcoin and Litecoin are examples of
technologies with single asset compatibility.
These technologies are limited to their
own underlying digital currency, but can also have off-chain solutions to interoperate with
other currencies, in order to execute transactions or to make use of smart contracts. There
are also solutions like coloured-coins.61
(3) Convertible Multiple Assets.
Other technologies like Counterparty and Ardor do have
19 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
their own underlying currencies or tokens to execute tasks. However, these technologies
also enable the possibility of exchange of assets expressed in others beside those native
to the platform. This approach of multiple, convertible currencies has the advantage of
allowing for exchange markets be directly incorporated into the system.
6.2. Tokenisation—A token acts as a digital bearer bond, whose ownership is determined by
the data embedded in the blockchain. Ownership of the tokens is transferable between holders
using other transactions with associated “transfer” metadata. This does not require the approval
of any other authority. The possibility of tokenisation enables a range of possible use cases for
blockchain technologies outside the purely ﬁnancial world.52, 55, 62–64
We identify three possible layouts for Tokenisation:
(1) No Tokenisation Present.
Bitcoin does not have native technologies that enable tokeni-
sation and requires third-party technologies.
(2) Tokenisation Through Third-Party Add-Ons.
Bitcoin plus Colour-Coin enables the
existence of tokenised transactions in the Bitcoin blockchain.
Said solution is based on
the cryptographic nature of Bitcoin addresses and the script language.
The tokenisation possibilities together with the extensions of metadata are
available in several implementations and constitute the backbone of blockchain-based
property registries. The most paradigmatic example is Ethereum, where the creation
of a new token is produced by means of the creation of a smart contract. Thanks to
this ﬂexibility, and the extreme possibilities for the expansion of such a platform, the
conditions for creation of new tokens are countless.
6.3. Asset Supply Management—The process of digital asset (i.e., cryptocurrency) creation
varies across different blockchain technologies. Each approach has taken different economic
frameworks, in most cases ﬁxing a speciﬁc monetary policy for the future of a particular system.
It is also generally a pillar of the incentive scheme that users have to participate (or not) in the
We identify three possible layouts for Asset Supply Management:
The most replicated system in the world of blockchain is the
limited supply as introduced in Bitcoin. Not only does the supply grow sub-linearly
over long periods of time (in contrast to what occurs in normal ﬁat currencies), but it is
designed to have a well-deﬁned limit. It is important that, while this incentivises users to
adopt the technology and contribute to the process of veriﬁcation—for which they receive
remuneration—on the other hand, it also creates an incentive to hoard the asset, limiting
Very few blockchain-based digital currencies have attempted
to create an unlimited supply, and those that have (for example Dogecoin or Freicoin)
have not seen wide acceptance.
Some altcoins (with the purpose of funding the development of a platform,
or with the sole idea of proﬁting) have distributed all the assets before the starting of the
system. From that point on, a reward system induces some kind of redistribution.
20 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
The alignment of the interoperability, intraoperability, governance, and script language determine
the future ecosystem of the blockchain network and the integration possibilities of a variety of
blockchain related technologies. Figure 1 (see above, page 107) illustrates the subcomponents
forming the component Extensibility:
• Script Language
7.1. Interoperability—Interoperability illustrates the overall capability of blockchains to
exchange information with other systems, outside of blockchains. It allows inﬂow, outﬂow, and
information retrieval of data providers that are not necessarily a blockchain-based system, e.g.,
ﬁnancial data providers.65 We identify three possible layouts for Interoperability:
(1) Implicit Interoperability.
This occurs when the smart contracts that specify conditions
under which a particular transaction (or event) is to take place can be written in a Turing-
complete blockchain script language. In this context, implicitly any kind of condition
can be speciﬁed, even those involving speciﬁc statuses in other systems. This implies an
(albeit cumbersome) way of interaction between a blockchain solution and any API tool
(2) Explicit Interoperability.
If the script language is not Turing complete or the system has
speciﬁc tools implemented that enable interoperability with the real world (like Bitcoin
with Counterparty), then we term this explicit interoperability, as it is brought purportedly
into the system as one of its design principles.
(3) No Interoperability.
A blockchain without any possibility for interacting with other
systems. As implemented, Bitcoin in the absence of external solutions (i.e., off-chain
layers) has no interoperability implemented. It applies to most existing blockchain-based
systems whose script language is not Turing complete.
7.2. Intraoperability—Intraoperability illustrates the overall capability of blockchains to
exchange information with other blockchains. It allows inﬂow, outﬂow, and exchange of data
between different blockchains.66 We identify three possible layouts for Intraoperability:
(1) Implicit Intraoperability.
This occurs when the smart contracts that specify conditions
under which a particular transaction (or event) is to take place can be written in a Turing-
complete blockchain script language. In this context, implicitly any kind of condition can
be speciﬁed, even those involving speciﬁc statuses in other blockchains.
(2) Explicit Intraoperability.
If the script language is not Turing complete but is speciﬁcally
designed to allow for intraoperability, then we term this explicit intraoperability, because
it is brought purportedly into the blockchain and is one of its design principles. An
example of this is Bitcoin with Counterparty.
(3) No Intraoperability.
A blockchain without any possibility for interaction with other
blockchains. As implemented, Bitcoin in the absence of external solutions has no intraop-
erability implemented. Solutions for non-intraoperable blockchains rely on: 1) Trusted
proxies to connect blockchains; 2) Pegged blockchain systems; 3) Distinguishing tokens
within the same blockchain based system.
21 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
7.3. Governance—Effective governance rules are crucial for the successful implementation
of blockchains and for their capability to adapt, change and interact. As blockchain deployment
structures (public chain, private chain, consortium chain) are different, their management patterns
are also quite different. We identify two types of governance rules: 1) technical rules of self-
governance, deﬁned by the participants. Technical rules are composed of software, protocols,
procedures, algorithms, supporting facilities and other technical elements; 2) regulatory rules,
deﬁned by external regulatory bodies composed of regulatory frameworks, provisions, industry
policies, and other components.
Regulatory rules are by deﬁnition not technical in nature
and therefore outside the scope of this taxonomy. We focus instead on technical rules which are
particularly interesting for the way they feed back into the proposed technological solutions. We
identify three possible layouts for Governance:
In this case, open communities of protocol developers (fol-
lowing open-source principles) and validators (very often in coordination with the
blockchain foundation) coordinate upgrades and technical adjustments of the blockchain.
For example, Bitcoin is mainly maintained by a team of core developers who in co-
ordination with miners agree on changing parameters or other settings of the Bitcoin
network. Ethereum and Hyperledger (backed up by the Linux Foundation) also follow an
open-source community model. Open-sources governance has been criticized for turning
out to become sometimes too centralized and obstructive.
For example, the Bitcoin
protocol core developers can veto external proposals to change rules and big mining pools
can signaling support for an upgrade earlier than expected.
. Since blockchain technologies are very versatile and can be applied to many
business cases, enterprises with a measure of technical strength (e.g., IBM and Microsoft)
have proposed themselves as technical solution providers for blockchain architectures
(i.e., providers of proprietary hardware and software systems and basic services). In
these cases, the technical rules of blockchain governance are dictated by the companies
according to their business goals. For example, in 2015 Microsoft collaborated with
ConsenSys to create an Ethereum blockchain technology service and implemented it as
part of the Microsoft Azure service (EBaaS) to provide distributed ledger technology
trials for enterprise customers, partners and developers. In order to protect their often
proprietary blockchain architectures, these companies generally apply also for patents.
According to a 2016 World Economic Forum report, 2,500 patents on this topic have been
ﬁled from 2014 to 2016.2
. This is the blockchain governance model proposed by industry consortia
(e.g., B3i, R3) composed of companies with common business or technological progress
demands. The alliance model has the scope to create shared technology platforms and
to build common business models and standards. Only companies that meet certain
criteria (e.g., payment of the fees, qualiﬁcation of the organisation) are given license to
collaborate to set the technical rules of blockchain governance. Those companies join
together to promote commercial and technological progress in the area of blockchain
technology to their mutual beneﬁt based on common contribution.
7.4. Scripting Language—Widespread programming languages are Turing-complete, which
in formal terms refers to the fact that it is possible to implement an algorithm on it to simulate any
Turing machine. These are therefore general purpose languages, in which arbitrary computations
22 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
can be performed. Languages that are not of this kind, are so because of design reasons which
aim to prevent speciﬁc behaviours of code execution, like undeﬁned termination.
Blockchain systems make it possible to modify the conditions under which certain information
(e.g., transactions) will be included in the public record. These conditions must be speciﬁed
in an algorithmic manner, and in some contexts are termed smart contracts. These algorithms
are elicited in a scripting language designed purely for this purpose. Therefore the intended
ﬂexibility given to the users, with respect to the scope that the algorithm can develop, affects
tremendously the degree of freedom to create conditions for some actions to occur (on the one
hand) and the hypothetical computational effort that may be necessary to assess if a particular
condition is fulﬁlled or not.71
It is worth remarking here that the degree to which the scope of the scripting language is
limited is another design decision developers must carefully consider before the implementation
of the blockchain, as abrupt changes (or bugs) may undermine the logic of particular transactions.
We identify four possible layouts for Scripting Language:
(1) Turing Complete.
Ethereum refers to a suite of protocols that deﬁne a platform for
decentralised applications. With respect to scripting languages, on the one end of the
spectrum, the Ethereum Virtual Machine (EVM) can execute code of arbitrary algorithmic
complexity. In the terms described above, Ethereum is “Turing complete,” because
developers can create applications that run on the EVM. Furthermore, Counterparty also
uses Ethereum’s entire smart contract platform to enable users to write Turing complete
smart contracts. It has been pointed out that there exist scalability and security concerns
regarding the usage of Turing complete scripting languages in blockchain systems.
of this writing, these have not been resolved.
(2) Generic Non-Turing Complete
. When designing Bitcoin, a decision was made to keep
the scripting language limited in scope, to reduce the impact of these calculations on
the efﬁciency of the system. It is therefore a non-Turing complete language, and most
blockchain implementations have followed this path. There is no connectivity in these to
so-called “oracles” that allows data to be obtained from sources that gather data that is
exogenous to the blockchain.
(3) Application-Speciﬁc Non-Turing Complete
. There are some non-Turing complete lan-
guages that are more expressive than the generic ones and purposely designed for certain
cases. By restricting the language to be only able to write programs relevant to speciﬁc
limited cases, the potential outputs of those programs become predictable. This allows
those outputs to be queried and easily analysed. One example is Digital Asset Modelling
Language (DAML) which is designed to codify only ﬁnancial rights and obligations for
execution in private networks. DAML is also more expressive than Bitcoin’s scripting
language and easier to read for a non-technical audience.
(4) Non-Turing Complete + External Data.
There exists a third category, barely used so
far, that (while keeping the nature of the scripting language non-Turing complete) allows
for the existence of oracles. These oracles are considered trustful sources and add a layer
of simpliﬁcation to the validation to be performed by the language, empowering above
Turing-completeness (as long as the oracles are reliable). This layout is thereby deemed
“non-Turing Complete + External Data.”
23 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
8. Security and Privacy
The recent evolution and new implementations of blockchain systems bring risks, both technical
and operational, associated with security and privacy. Thus, we group together security and
privacy as two interrelated faces of the same problem. Similarly, ISO TC 307 has created a
dedicated Working Group on “Security and Privacy.”73
Security of blockchain systems is a matter of signiﬁcant concern. Cryptocurrencies, the
most widely deployed application of blockchain systems, have suffered from cyberattacks which
became possible because of the mismanagement of sensitive data and the ﬂawed design of
Without going into the detailed distinction between “risks,” “threats,” “attack
surfaces,” and “vulnerabilities,” the security of blockchain systems generally comprises: 1) Infor-
mation mismanagement (alternation, deletion, destruction, disclosure, etc.); 2) Implementation
vulnerabilities (including cryptomechanisms’ implementation vulnerabilities, run-time leakage
of information, etc.); 3) Cryptographic mechanism mismanagement (including use of weak
algorithms, key disclosure, etc.); 4) User privilege mismanagement. For a recent comprehensive
survey speciﬁcally detailing the security and privacy aspects of Bitcoin and its related concepts,
we refer the readers to Conti (et al.) “A Survey on Security and Privacy Issues of Bitcoin”
When we speak of privacy we are referring to “freedom from intrusion into the private
life or affairs of an individual when that intrusion results from undue or illegal gathering and use
of data about that individual,” as cited in ISO 25237 and other ISO standards. These privacy
principles apply to any ICT system containing or processing PII, including blockchain systems.
Figure 1 (above, page 107) illustrates the subcomponents forming the component Security &
• Data Encryption
• Data Privacy
8.1. Data Encryption—By Data Encryption we refer to cryptographic primitives. Cryp-
tographic primitives (cryptographic algorithms) are used to ensure authenticity, integrity, and
order of events. For example, Bitcoin blockchain uses the ECDSA digital signature scheme
for authenticity and integrity, and the SHA-2 hash function for integrity and order of events.
Hash functions are also commonly used as a part of a Proof-of-Work consensus mechanism. We
identify two major layouts for Data Encryption:
SHA stands for Secure Hash Algorithm. In its two incarnations, SHA-256 and
SHA-512, SHA (originally developed by the National Security Agency of the United
States of America) is the most widely-used for hashing functions, likely stemming from its
use in Bitcoin.
When applied to hash transactions, it requires a piece of information
from the issuer, i.e., the public key for the validation to take place.78
The Zero-Knowledge—Succinct Non-interactive Argument of Knowl-
edge is a newer technology where no data has to be provided to validate a speciﬁc hash,
but the hash itself serves as proof. Therefore both the hashed message and the encrypted
one are sufﬁcient proof of existence. This novel approach anonymises the individual
information to a much greater degree.
8.2. Data Privacy—Although public/private key infrastructures and other measures like
hashing functions should ensure that only the intended recipient can read the message and have
24 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
access to the content of the transaction, the research shows that blockchain transactions (e.g.,
in Bitcoin) can be linked together in order to extract additional information and eventually
also the identity of the participants.
Indeed, there exists an inevitable tradeoff between a
decentralised peer-validated system and the security and privacy of information. In this regard,
several alternative solutions have been proposed to “encrypt” the data in such a way that even
though computations and transactions occur in plain sight, the underlying information is kept
completely obfuscated. Obfuscation is a way of turning any program into a “black box.” This
is equivalent to the original program: it runs the same “internal logic” and provides the same
outputs for the same inputs, but information on the data and processes is inaccessible. Of course
there exists a strong interrelation between Data Privacy and Data Encryption. Based on the
solutions proposed so far to enhance Data Privacy, we identify two possible layouts:
(1) Built-In Data Privacy
. With built-in data privacy we include all those blockchains that
by default provide obfuscation of information. For example ZeroCash uses built-in
zero-knowledge cryptography to encrypt the payment information in the transactions.
Although ZeroCash payments are published on a public blockchain, the sender, recipient,
and amount of a transaction remain private. Alternatively, blockchains like Enigma (a
project that seeks to implement the “secret sharing” DAO concept) uses built-in secure
multi-party computation guaranteed by a veriﬁable secret sharing scheme.
In this case,
the data can be split among Nparties in such a way that M<Nare needed to cooperate
in order to either complete the computation or reveal any internal data in the program
or the state. But
parties cannot recover any information at all (which implies the
need for trust on the majority of the participants to be honest). Finally, CORDA by R3
proposes a Node to Node (
) system characterised by encrypted transactions where
only the parties involved in the transaction have access to the data.83 This is suitable for
ﬁnancial transactions where a high degree of conﬁdentiality is required. Third parties like
central banks or other market authorities may have access to the data by invitation only.
(2) Add-On Data Privacy
. In this case, pseudonymous or public blockchains must resort to
external solutions in order to obfuscate the information. One method is the mixing service
like Coinjoin. The principle behind this method is quite simple: several transactions are
grouped together so to become a unique
transaction. If for example, Alice wants
to send one coin to Bob, and Carla wants to send one coin to David, a mixing transaction
could be established whereby the addresses of Alice and Carla are both listed as inputs,
and the addresses of Bob and David are listed as outputs in one unique transaction. Thus,
when inspecting the
transaction from outside it is impossible to discern who is the
sender and who the recipient.
Alternative to mixing services, secret sharing allows
data to be stored in a decentralised way across
parties such that any
work together to reconstruct the data, but
parties cannot recover any information at
all. Alternative add-on data privacy tools include ring signatures and stealth addesses
which hide the recipient of a transaction and can be used by any blockchain.
signatures—ﬁrstly introduced by Rivest, Shamir, and Tauman in “How to Leak a Secret”
—and its variant, linkable ring signatures, allow users to hide transactions within
a set of others’ transactions. In this case the transaction is tied to multiple senders’ private
keys but only one of them is the initiator. Thus, the veriﬁer may only identify that one
of them was a signer, but not who exactly that was. In the case of stealth addresses, a
25 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
receiver generates a new dedicated address and a “secret key” and then sends this address
to someone who he wants payment from. The sender use the address generated by the
receiver plus a “nonce” (one time random number) in order to generate the address he/she
will send funds to. The sender communicates the nonce to the receiver who can unlock
the address by using the nonce and the secret key generated earlier. Monero is an example
of a blockchain that aims to achieve privacy through the use of traceable ring signatures
and stealth addresses.
The codebase of blockchain technologies delivers information about which challenges a developer
could face and what kind of changes the underlying programming language could undergo.
Therefore the main component ‘Codebase’ is essential to align and increase the efﬁciency of
blockchain-related IT architectures. Figure 1 (above, page 107) illustrates the subcomponents
forming the component Codebase:
• Coding Language
• Code License
• Software Architecture
9.1. Coding Language—Coding language illustrates the interconnectivity of programming
languages of blockchain technologies. We identify two possible layouts for Coding Language:
(1) Single Language.
Bitcoin has released Bitcoin Core version 0.13.1 in the underlying
coding language C++. As Bitcoin is open source, implementations occurred (much less
popular than the original codebase) in different languages (like Java).
(2) Multiple Languages.
Ethereum uses C++, Ethereum Virtual Machine Language, and
Java, and Go-based SDKs for communicating with Horizon. There are also community-
maintained SDKs for Ruby, Python, and C-Sharp.
9.2. Code License—The Code License illustrates the possibility of changes to the source
code of the underlying technology. We identify thee possible layouts for Code License:
Regardless of the exact licence used for speciﬁc projects, we refer only to
the openness in the source code as the only differentiating factor. Bitcoin core developers
have continuously licenced the source code under the MIT licence. Counter-intuitively, a
permissive licence like the MIT one (in which other developers can take the source code
and fork it) eventually prevents multiple implementations. It also allows for continued
development, more code growth, and adoption at a faster pace. Ripple and Stellar have
licensed their codes with the ISC License, which is another permissive licence.
For private implementations of blockchain-based systems, the source
code is not necessarily openly distributed. Just as an example, most the blockchains run-
ning on the Ethereum Enterprise Alliance (rather than on the public Ethereum blockchain)
use closed-source codes. In this case, risking the existence of unadressed bugs or unre-
ported characteristics that may violate the expected conditions of use and functioning, the
code may be kept outside of reach for users.
26 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
9.3. Software Architecture—The Software Architecture refers to the high level structures of
the blockchain system. Each structure is composed of software elements, the relations between
them, and the properties that emerge from those elements and relations. The choice of the
software architecture is very important in order to better manage changes once implemented.
Software architecture choices include speciﬁc structural options among the possibilities that are
available for software design.
We identify two possible layouts for Software Architecture:
(1) Monolithic Design
. In this case, all the aspects of a decentralised ledger (P2P connec-
tivity, the “mempool” broadcasting of transactions, criterion for consensus on the most
recent block, account balances, nature of smart contracts, user-level permissions, etc.) are
handled by a blockchain built as a single-tier software application without modularity.
Examples of blockchains with monolithic design include Bitcoin and Ethereum. These
architectures suffer from a lack of extensibility on the long run.
(2) Polylithic Design
. The Polylithic approach decouples the consensus engine and P2P
layers from the details of the application state of the particular blockchain application. For
example, in Tendermint the blockchain design is “decomposed.” Between the application
process and its application-agnostic “consensus engine” (TenderminCore), it offers a
very simple API which enables it to run Byzantine fault-tolerant applications, written
in any programming language, not just the one the consensus engine is written in. Also
Hyperledger Fabric follows a polylithic design as it is composed of interchangeable
modules representing different components of blockchain technologies.
10. Identity Management
The main component Identity Management ensures secure access to sensitive data to establish
a suitable governance model for the blockchain. This is a complex matter, as different levels
of authority, accountability and responsibility are attached to different type of participants
(e.g., users, administrators, developers, validators, etc). Generally, the set of rules are deﬁned
and enforced through mechanisms intrinsic to the system itself (on-chain governance). The
subcomponents also eventually determine the concept of digital identity that users end up having
within the systems. Figure 1 (above, page 107) illustrates the subcomponents forming the
component Identity Management:
• Access and Control Layer
• Identity Layer
10.1. Access and Control Layer—When establishing the right governance structure for a
blockchain it is important to consider the ledger construction. Depending on its purpose, the
ledger could be run by a central authority and governed by it, or it could be run in a decentralised
fashion according to a set of governance rules adhered to and enforced by participants on the
blockchain network. The governance structure determines the authorisation and the control policy
management functions. Those rules provide permission for users to access to or use blockchain
resources. Those are a set of rules that manage user, system and node permissions that must be
followed in security-related activities. Blockchains may have different permissions according to
different levels of access to and control of data. The distinguishing features must answer to the
27 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
•Which users have “read” access?
•Which users have “write” access?
s it there anyone who can “manage consensus” (i.e., update and maintain the integrity of
According to the set of governance rules, we may have different system designs that reply to the
above questions in a different way in order to better serve either a public or a private interest of
either a general (like in the case of Ethereum) or a special (like in the case of Corda) purpose.
On one side, private blockchains are generally those with a set of constrained “read/write” access
alongside a consensus algorithm which allows only a pre-selected group of people to contribute
and maintain blockchain integrity. Instead, public blockchains do not control “read/write” access
or in the consensus algorithm for any given set of participants. Nevertheless, this does not mean
that certain permission structures can not be implemented as part of a speciﬁc application.
Although different variations are possible, the authority to perform transactions on a
blockchain generally belongs to one of the following main models of the Access and Control
(1) Public Blockchain
. In this case, there is no preference in access or in managing con-
sensus. All participants (nodes), have “read/write” access and without any control can
contribute to the update and management of the ledger. An example of a blockchain in
this model is Bitcoin, where every participant can either choose just to use the blockchain
to exchange Bitcoins (or other data on the top of it, in general by means of third-party
technologies), run a full node, or even become a miner to participate in the process of
(2) Permissioned Public Blockchain
. In this case “read” access is enabled for all users,
however “write” access and/or “consensus management” require permission by a pre-
selected set of nodes. Ripple belongs to this group, as to validate transactions a participant
need to be part of the so-called Unique Node List. Some other examples include Ethereum
and Hyperledger Fabric which is used for the exchange of tangible assets (real estate and
hardware) and intangible assets (contracts and intellectual property) between enterprises.
(3) Permissioned Private Blockchain
. In this case, “read/write” and “consensus manage-
ment” rights can only be granted by a centralised organisation. One example of this type
is Monax (formerly known as Eris).
10.2. Identity Layer—The onboarding and offboarding of nodes / entities to a blockchain
network is handled differently by various software solutions. By identiﬁcation we mean the
capability to identify an entity uniquely in a given context. Digital identity can be deﬁned as a
set of identifying attributes of an entity that together enable the unique identiﬁcation of the entity
in a context (UID). A vital part of any identity system (and most information systems) is that a
UID is managed throughout the entity’s lifecycle to protect it from negligence and fraud, and to
preserve the UID’s uniqueness. A UID can then be assigned to the identity and used to link or
bind the entity to the claimed identity and to any digital credential (software or hardware) issued
to the entity. This digital credential acts a trusted proxy for the physical or logical entity and
is used to support a wide range of personal and trust-related functions such as authentication,
encryption, digital signatures, application logins and physical access control.
AML and KYC procedures—generally required to process personal or private data e.g.,
medical data, bank information, or other personal data—are the key aspects to consider when
28 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
looking at the Identity Layer. We identify two possible layouts:
Know-Your-Customer (KYC) and Anti-Money-Laundering (AML)-compliant
blockchains have the ability to validate organisations and their attribute data from
authoritative sources to ensure the quality of data written to the blockchain and linked
to identiﬁers in the blockchain. One example is Stellar, which sets requirements for
all integrators to implement KYC/AML identity veriﬁcation processes to increase
the transparency of the Stellar network participants. For another example, Ripple
forces its ﬁnancial services partners to implement an identity layer to verify the user
information. The ﬁnancial services partners have to perform due diligence, depending on
the requirements they must fulﬁll.
A common misunderstanding regarding the level of anonymity within
Bitcoin networks is that the majority of the users do not distinguish between anonymity
and pseudonymity. The Bitcoin protocol has no identity layer to identify the users, which
could lead to misuse of Bitcoins and money laundering activities through its blockchain
network, but to control approaches to anonymity in Bitcoin and other cryptocurrencies.
Regal Reid and Martin Harrigan have been able to demonstrate that several pseudonymous
addresses can be linked to one single user.
See also Tasca, Liu, and Hayes, “The
Evolution of the Bitcoin Economy” (2016) for a Bitcoin transaction-path driven user-
11. Charging and Rewarding System
Blockchain systems incur operational and maintenance costs that are generally absorbed by the
participants in the network. Different kinds of cost models are applied according to: 1) the
architectural conﬁguration design; 2) the governance system; and 3) the data structure and the
computation required on-chain. One of the costs common to the wide majority of blockchains is
the veriﬁcation cost. This is required to sustain the validation process of the transactions that
compete to be appended to (and never removed from) the ledger. The potential ﬁnancial costs
incurred when taking part in a blockchain platform require an incentive scheme that maintains
consistency in the cost structure across the different stakeholders. Figure 1 (above, page 107)
illustrates the subcomponents of the component we label the Charging and Rewarding System:
• Reward System
• Fee System
–[2.1] Fee Reward
–[2.2] Fee Structure
11.1. Reward System—This subcomponent illustrates the reward mechanisms automatically
put in place and triggered by the systems in order to compensate active members contributing
to data storage or transaction validation and veriﬁcation. We identify two possible layouts for
(1) Lump-Sum Reward.
Individuals taking part in the storage, validation, and/or veriﬁcation
processes (e.g., in Bitcoin veriﬁcation is only performed by users called miners) may be
rewarded for their action. For example, in Bitcoin, the ﬁrst transaction in each block is
called the coinbase, and the recipient is the user (or users) who created the block. The
29 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
lump-sum reward can be ﬁxed, like in Enigma, or variable, like in Bitcoin.
(2) Block + Security Reward.
In other blockchain-based technologies, like Ethereum, the
blockchain rewarding system includes, besides the block reward, a reward for including
forked blocks that are still valid in the validation. The purpose of the design is to
incentivise cross-validation of transactions (crucial in a setting where validation can be
11.2. Fee System—A different kind of reward is that provided directly by the users to other
participants in the system when launching any request in the network for storage, data retrieval,
or computation and validation. With regards to this, we identify two sub-subcomponents: Fee
Reward and Fee Structure.
11.2.1 Fee Reward
AFee Reward is composed of the fees that users are required to contribute when using a
blockchain. The fee system has been shown to play an important role in the way veriﬁers
This kind of design-time consideration ought not to be neglected, though it often is.
We identify three possible layouts for Fee Reward:
(1) Optional Fees.
In Bitcoin and related technologies users can optionally pay a voluntary
fee for the validation process.
This fee is optional, but it is assumed that the larger the
fee is, the lower the processing time for a transaction (i.e., the less time it will take for the
transaction to be added to a block), as miners will be more incentivised to include it in a
block. Moreover, given that the coinbase reward halves approximately every four years,
currently, the reference Bitcoin client refuses to relay transactions with a zero transaction
(2) Mandatory Fees.
Some systems like Stellar force all users to include fees in any transac-
tion added into the system.
(3) No Fees.
In comparison, Hyperledger Fabric is a blockchain solution for businesses,
which combines a permissioned network and an identity layer without any transaction
11.2.2 Fee Structure
When provided by the system, fees can follow either a ﬁxed or a variable structure. There are
two alternative layouts for Fee Structure:
(1) Variable Fees.
In this case, the fee is somehow linked to the “size” of the request. In
Bitcoin, the larger the transaction size, the higher the fee the user will pay in order to
compensate for taking up space inside the block. Miners usually include transactions
with the highest fee-per-byte value ﬁrst. The user can decide how many “Satoshis” to pay
per byte of transaction (a Satoshi is 0.00000001 Bitcoins, or one hundred-millionth of a
Bitcoin). For example, if the transaction is 1,000 bytes and the user pay a fee of 300,000
Satoshis, he/she will be in the 300 Satoshi/bytes section (300,000/1,000=300.00). At the
time of writing, this implies that the transaction will be included in the next 2 blocks
(i.e., within about 20 minutes). However, to avoid queuing, the user can opt to increase
the fee. At the time of writing, the fastest and cheapest transaction fee is currently 360
30 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
Satoshis/byte. For an average transaction size of 226 bytes, a fee of 81,360 Satoshis is
currently the cheaper fee in order to get the transaction included in the ﬁrst available
block without delays. Other blockchains apply variable fees and follow similar rules to
(2) Fixed Fees.
In this case, the fee is linked to the request, not to its “size.” For example,
in Enigma every request in the network for storage, data retrieval, or computation has
a ﬁxed price, similar to the concept of Gas in Ethereum. However, since Enigma is
a Turing-complete system, the fee can be different depending on the speciﬁc request.
Another example of a blockchain with a ﬁxed transaction fee is Peercoin which requires a
ﬁxed 0.01 PPC per kilobyte.
In the 21st century, blockchain technologies will likely affect most, if not all, business areas:
the energy industry,
automation, and even robotics will be involved. After the creation of the Internet, it represents
perhaps the most prominent technological innovation, and it will shape the forthcoming products
and services in nearly every industry. Since the introduction of Bitcoin in 2009, the awareness of
blockchain technologies has considerably increased. The ﬁrst mover regarding the adoption of
blockchain was the ﬁnancial industry. This is explained by the fact that blockchain technology
enables cost reductions and increases to efﬁciency in several business processes (both internal
and external) for ﬁnancial institutions. An example of the outsized impact of blockchain in the
ﬁnancial industry can be seen in the networks of global payments: monetary transactions in
exchange for goods, services, or legal obligations between both individuals and economic entities.
Beyond payments, blockchains allow for real-time settlements, which reduce operational costs
for banks. Further, the immutability of the blockchain reduces the risk of fraud, and banks can
use sophisticated smart contracts to capture digital obligations and to eliminate operational errors.
Global payments are just a fraction of the overall use cases in the ﬁnancial industry. In addition
to this, many other industries, including the public sector, are now looking at blockchain-enabled
solutions for their own processes. This trend is causing a proliferation of multiple blockchain
architectures which often are not interoperable and are built according to different engineering
designs. Lately, software architectures, companies, and regulators have begun to realise the need
for standardisation of some of their components. This is becoming a necessary step for blockchain
technologies in order to: 1) gain global adoption and compatibility, 2) create cross-industry
solutions, and 3) provide cost-effective solutions. As always with standardisation processes,
their creation must ﬁnd equilibrium between different parties. But in this particular case, the
open-source community that brought forward this disruptive technology—and which continues
to develop the most implementations—should play a crucial role, as heralds of the advancement
of blockchains and blockchain technologies.
Based on the review of the current literature on blockchain technologies, our work is an
early stage analysis across existing software architectures with the aim of proposing a taxonomy:
a reference architectural model for blockchains and their possible conﬁgurations. Organized
on the basis of component-based design, the blockchain taxonomy deconstructs the various
31 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
blockchains into individual functional or logical components and identiﬁes possible different
layouts. It is hoped that this blockchain taxonomy will assist in the exploration of design domains,
in the implementation, deployment, and measurement of the performance of different blockchain
Our work sheds light on the current proliferation of non-interoperable blockchain platforms
and on the need for—and current discussions about—blockchain standards. Although our work
contributes to the ongoing efforts to set blockchain standards, we do not conclude by insisting on
the need for a set of standards now. This process generally takes several years in order to produce
concrete solutions (even 10 years for complex subjects). Therefore, we think that our taxonomy
represents a timely, honest intellectual exercise to be used as preliminary supporting material for
all those interested in reducing blockchain complexity. At the same time, we are aware that our
taxonomy tree, although hopefully very useful, is very preliminary and likely the ﬁrst version of
subsequent more complex evolutions.
PT and CJT thank Harvey R. Campbell for his invaluable comments on a previous version of this
paper. They also thank Alessandro Recchia and Thayabaran Thanabalasingham for their support
and contribution. PT acknowledges the University College London for ﬁnancial support through
the EPSRC program EP/P031730/1. CJT acknowledges the University of Zurich for ﬁnancial
support through the University Research Priority Programme on Social Networks.
Notes and References
1Nakamoto, S. “Bitcoin: A Peer-to-Peer Electronic Cash System.” (2008) (accessed 29 October 2018)
2McWaters, R. J. “The Future of Financial Infrastructure: An Ambitious
Look at How Blockchain Can Reshape Financial Services.” World Economic Fo-
rum (2016) (accessed 29 October 2018) https://www.weforum.org/reports/
3Tasca, P. “The Dual Nature of Bitcoin as Payment Network and Money.” SUERF Conference Proceedings
2016/1 ”Cash on Trial”. (2016) http://dx.doi.org/10.2139/ssrn.2805003.
4Meguerditchian, V. “Roadmap for Blockchain Standards Report–March 2017.” Standards
Australia (2017) (accessed 29 October 2018) https://www.standards.org.au/getmedia/
6Tasca, P. “Digital currencies: Principles, Trends, Opportunities, and Risks.” SSRN (2015) (accessed 29
October 2018) https://ssrn.com/abstract=2657598.
7Marian, O. “A Conceptual Framework for the Regulation of Cryptocurrencies.” University of Chicago
Law Review Online 82.4 53 (2015) https://chicagounbound.uchicago.edu/uclrev_online/vol82/
8Aste, T., Tasca, P., Di Matteo, T. “Blockchain Technologies: The Foreseeable Impact on Society and
Industry.” Computer 50.9 18–28 (2017) http://doi.ieeecomputersociety.org/10.1109/MC.2017.
32 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
9Barber, S., Boyen, X., Shi, E., Uzun, E. “Bitter to Better—-How to Make Bitcoin a Better Currency.” In
A. Keromytis (Ed.), International Conference on Financial Cryptography and Data Security. Berlin: Springer
399–414 (2012) https://doi.org/10.1007/978-3-642-32946-3_29.
10 Walch, A. “The Path of the Blockchain Lexicon (and the Law).” Review of Banking and Financial Law 36
713 (2017) https://www.bu.edu/rbfl/files/2017/09/p729.pdf.
11 Clack, C. D., Bakshi, V. A., Braine, L. “Smart Contract Templates: Essential Requirements and Design
Options.” arXiv (2016) (accessed 29 October 2018) https://arxiv.org/abs/1612.04496.
12 Cawrey, D. “Why New Forms of Spam Could Bloat Bitcoin’s Block Chain.” CoinDesk (2014) (accessed
13 January 2017) http://www.coindesk.com/new-forms-spam-bloat-bitcoins- block-chain/.
13 Xu, X., et al. “A Taxonomy of Blockchain-Based Systems for Architecture Design.” In 2017 IEEE Interna-
tional Conference on Software Architecture. 243–252 (2017) https://doi.org/10.1109/ICSA.2017.33.
14 Otto, K. N., Wood, K. L. “Product Evolution: A Reverse Engineering and Redesign Methodology.” Re-
search in Engineering Design 10.4 226–243 (1998) https://doi.org/10.1007/s001639870003.
15 Bonneau, J., Miller, A., Clark, J., Narayanan, A., Kroll, J. A., Felten, E. W. “SoK: Research Perspectives
and Challenges for Bitcoin and Cryptocurrencies.” In 2015 IEEE Symposium on Security and Privacy. IEEE
104–121 (2015) https://doi.org/10.1109/SP.2015.14.
16 Mattila, J. “The Blockchain Phenomenon–The Disruptive Potential of Distributed Consensus Architec-
tures.” The Research Institute of the Finnish Economy (2016) (accessed 29 October 2018) https://ideas.
17 Wright, A., De Filippi, P. “Decentralized Blockchain Technology and the Rise of Lex Cryptographia.”
SSRN (2015) (accessed 29 October 2018) https://dx.doi.org/10.2139/ssrn.2580664.
18 Rabbit. “So You Want to Run Rippled?” Ripple (2018) (accessed 1 November 2018) https://
19 No Author. “History Sharding.” Ripple (2018) (accessed 1 November 2018) https://developers.
20 No Author. “Validator Details.” Ripple (2018) (accessed 1 November 2018) https://xrp.media/
21 Driscoll, K., Hall, B., Sivencrona, H., Zumsteg, P. “Byzantine Fault Tolerance: from Theory to Reality.” In
S. Anderson, M. Felici, B. Littlewood (Eds.), International Conference on Computer Safety, Reliability, and
Security. Berlin: Springer 235–248 (2003) https://doi.org/10.1007/978-3-540-39878-3_19.
22 Castro, M., Liskov, B., et al. “Practical Byzantine Fault Tolerance.” In Proceedings of the Third Symposium
on Operating Systems Design and Implementation, New Orleans, USA, February 1999. 173–186 (1999)
23 Cristian, F. “Understanding Fault-Tolerant Distributed Systems.” Communications of the ACM 34.2 56–78
24 Fischer, M. J. “The Consensus Problem in Unreliable Distributed Systems (A Brief Survey).” In M. Karpin-
ski (Ed.), International Conference on Fundamentals of Computation Theory. Berlin: Springer 127–140
25 Tsukerman, M. “Proof of Stake versus Proof of Work.” (2015) (accessed 29 October 2018) https://
26 O’Dwyer, K. J., Malone, D. “Bitcoin Mining and its Energy Footprint.” In 25th IET Irish Signals and
Systems Conference 2014 and 2014 China-Ireland International Conference on Information and Communi-
cations Technologies (ISSC 2014/CIICT 2014). (2014) https://dx.doi.org/10.1049/cp.2014.0699.
27 Lewenberg, Y., Bachrach, Y., Sompolinsky, Y., Zohar, A., Rosenschein, J. S. “Bitcoin Mining Pools: A
Cooperative Game Theoretic Analysis.” In Proceedings of the 2015 International Conference on Autonomous
Agents and Multiagent Systems. Istanbul: International Foundation for Autonomous Agents and Multiagent
Systems 919–927 (2015) http://dl.acm.org/citation.cfm?id=2772879.2773270.
33 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
28 Eyal, I., Sirer, E. G. “Majority Is Not Enough: Bitcoin Mining Is Vulnerable.” In N. Christin, R. Safavi-
Naini (Eds.), Financial Cryptography and Data Security 18th International Conference, FC 2014, Christ
Church, Barbados, March 3-7, 2014. Berlin: Springer 436–454 (2014) https://doi.org/10.1007/
29 Dziembowski, S., Faust, S., Kolmogorov, V., Pietrzak, K. “Proofs of Space.” In R. Gennaro, M. Robshaw
(Eds.), Advances in Cryptology–CRYPTO 2015. Berlin: Springer 585–605 (2015) https://doi.org/10.
30 Park, S., Pietrzak, K., Alwen, J., Fuchsbauer, G., Gazi, P. “Spacecoin: A Cryptocurrency Based on Proofs
of Space.” IACR Cryptology ePrint Archive (2015) (accessed 29 October 2018) https://eprint.iacr.
31 Patterson, R. “Alternatives for Proof of Work, Part 2: Proof of Activity, Proof of Burn, Proof of Ca-
pacity, and Byzantine Generals.” Bytecoin (2015) (accessed 25 April 2017, no longer online but available
via Internet Archive Wayback Machine) https://web.archive.org/web/20160304055454/https://
32 No Author. “ripple.com.” Ripple (2015) (accessed 1 June 2015) http://www.ripple.com.
asi, A.-L., P´
osfai, M. Network Science. Cambridge: Cambridge University Press (2016).
34 Douceur, J. R. “The Sybil Attack.” In P. Druschel, F. Kaashoek, A. Rowstron (Eds.), Peer-to-Peer Systems.
Berlin: Springer 251–260 (2002) https://doi.org/10.1007/3-540-45748-8_24.
35 Decker, C., Wattenhofer, R. “Information Propagation in the Bitcoin Network.” In 2013 IEEE Thirteenth
International Conference onPeer-to-Peer Computing. IEEE 1–10 (2013) https://doi.org/10.1109/P2P.
36 Lamport, L., Shostak, R., Pease, M. “The Byzantine Generals Problem.” ACM Transactions on Program-
ming Languages and Systems 4.3 382–401 (1982) https://doi.org/10.1145/357172.357176.
37 Merkle, R. C. “A Digital Signature Based on a Conventional Encryption Function.” In C. Pomerance (Ed.),
Advances in Cryptology–CRYPTO ’87. Berlin: Springer 369–378 (1988) https://doi.org/10.1007/
38 Morrison, D. R. “PATRICIA—Practical Algorithm To Retrieve Information Coded In Alphanumeric.”
Journal of the ACM 15.4 514–534 (1968) https://doi.org/10.1145/321479.321481.
39 Wood, G. “Ethereum: A Secure Decentralised Generalised Transaction Ledger.” Ethereum (2014) (ac-
cessed 29 October 2018) https://ethereum.github.io/yellowpaper/paper.pdf.
40 Buterin, V., Ray, J. “Light Client Protocol: Background: Patricia Merkle Trees.” (2014) (ac-
cessed 10 November 2018), https://github.com/ethereum/wiki/wiki/Light-client-protocol#
41 No Author. “Developer : Block Chain Overview.” bitcoin.org (2015) (accessed 25 April 2017) https:
42 Xu, Q., Aung, K. M. M., Zhu, Y., Yong, K. L. “A Blockchain-Based Storage System for Data Analytics in
the Internet of Things.” In R. Yager, J. Pascual Espada (Eds.), New Advances in the Internet of Things. Berlin:
Springer 119–138 (2018) https://doi.org/10.1007/978-3-319-58190-3_8.
43 Tessone, C. J., Tasca, P. “A Parsimonious Model for Blockchain Consensus: Scalability and Consensus
Collapse.” (2017) Unpublished Working Paper UZH and UCL.
44 Tasca, P., Widmann, S. “The Challenges Faced by Blockchain Technologies–Part 1.” Journal of Digital
Banking 2.2 132–147 (2017).
45 Lombrozo, E., Lau, J., Wuille, P. “Segregated Witness (Consensus Layer).” (2015) Technical Report BIP-
141, Bitcoin Improvement Proposal https://github.com/bitcoin/bips/blob/master/bip-0141.
46 Garzik, J. “Segregated Witness in the Context of Scaling Bitcoin.” (2015) (accessed 29 October 2018)
34 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
47 Decker, C., Wattenhofer, R. “A Fast and Scalable Payment Network with Bitcoin Duplex Micropayment
Channels.” In A. Pelc, A. Schwarzmann (Eds.), Stabilization, Safety, and Security of Distributed Systems,
17th International Symposium, SSS 2015, Edmonton, AB, Canada, August 18-21, 2015, Proceedings. Berlin:
Springer 3–18 (2015) https://doi.org/10.1007/978-3-319-21741-3_1.
48 Poon, J., Dryja, T. “The Bitcoin Lightning Network: Scalable Off-chain Instant Payments.” See: https:
49 Lerner, S. D. “Segwit2Mb–combined soft/hard fork–Request For Comments.” (2017) (accessed 29 Octo-
ber 2018) https://lists.linuxfoundation.org/pipermail/bitcoin-dev/2017-March/013921.
50 Eyal, I. “The Miner’s Dilemma.” In 2015 IEEE Symposium on Security and Privacy. IEEE 89–103 (2015)
51 Tessone, C. J., Garcia, D. “Bitcoin: The Centralisation of a Decentralised Economy.” (2017) Unpublished
Working Paper UZH.
52 Catalini, C., Gans, J. S. “Some Simple Economics of the Blockchain.” SSRN (2017) (accessed 29 October
53 Sompolinsky, Y., Zohar, A. “Bitcoin’s Underlying Incentives.” Communications of the ACM 61.3 46–53
54 Garcia, D., Tessone, C. J., Mavrodiev, P., Perony, N. “The Digital Traces of Bubbles: Feedback Cycles
Between Socio-Economic Signals in the Bitcoin Economy.” Journal of The Royal Society Interface 11.99
20140623 (2014) URL https://dx.doi.org/10.1098/rsif.2014.0623.
55 Tsukerman, M. “The Block Is Hot: A Survey of the State of Bitcoin Regulation and Suggestions for the
Future.” Berkeley Technology Law Journal 30 1127 (2015) https://doi.org/10.15779/Z38CK4X.
56 Grinberg, R. “Bitcoin: An Innovative Alternative Digital Currency.” Hastings Science & Technology Law
Journal 4159 (2012) https://ssrn.com/abstract=1817857.
57 Selgin, G. “Synthetic Commodity Money.” Journal of Financial Stability 17 92–99 (2015) https://
58 Luther, W. J. “Is Bitcoin Intrinsically Worthless?” Journal of Private Enterprise 33.1 31–
45 (2018) http://journal.apee.org/index.php/2018_Journal_of_Private_Enterprise_Vol_
59 Luther, W. J. “Cryptocurrencies, Network Eeffects, and Switching Costs.” Contemporary Economic Policy
34.3 553–571 (2016) https://doi.org/10.1111/coep.12151.
60 Buterin, V., et al. “A Next-Generation Smart Contract and Decentralized Application Platform.” (2014)
(accessed 29 October 2018) https://github.com/ethereum/wiki/wiki/White-Paper.
61 Rosenfeld, M. “Overview of Colored Coins.” (2012) (accessed 29 October 2018) https://bitcoil.co.
62 Rohr, J., Wright, A. “Blockchain-Based Token Sales, Initial Coin Offerings, and the Democratization
of Public Capital Markets.” SSRN (2017) (accessed 29 October 2018) https://ssrn.com/abstract=
63 Adhami, S., Giudici, G., Martinazzi, S. “Why Do Businesses Go Crypto? An Empirical Analysis of Initial
Coin Offerings.” Journal of Economics and Business (in press) https://doi.org/10.1016/j.jeconbus.
64 Conley, J. P. “Blockchain and the Economics of Crypto-Tokens and Initial Coin Offerings.” Vanderbilt
University Department of Economics Working Papers (2017) (accessed 29 October 2018) https://ideas.
65 Dilley, J., Poelstra, A., Wilkins, J., Piekarska, M., Gorlick, B., Friedenbach, M. “Strong Federations: An
Interoperable Blockchain Solution to Centralized Third Party Risks.” arXiv (2016) (accessed 29 October
35 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
66 Chen, Z.-D., Zhuo, Y., Duan, Z.-B., Kai, H. “Inter-Blockchain Communication.” DEStech Transactions
on Computer Science and Engineering http://dx.doi.org/10.12783/dtcse/cst2017/12539.
67 Atzori, M. “Blockchain Technology and Decentralized Governance: Is the State Still Necessary?” Journal
of Governance and Regulation 6.1 https://dx.doi.org/10.22495/jgr_v6_i1_p5.
68 Davidson, S., De Filippi, P., Potts, J. “Disrupting Governance: The New Institutional Economics of Dis-
tributed Ledger Technology.” SSRN (2016) (accessed 29 October 2018) https://ssrn.com/abstract=
69 Wright, A., De Filippi, P. “Decentralized Blockchain Technology and the Rise of Lex Cryptographia.”
SSRN (2015) (accessed 29 October 2018) https://ssrn.com/abstract=2580664.
70 Tasca, P. “The Hope and Betrayal of Blockchain.” (2018) (accessed 9 November 2018) https://www.
71 Kim, H., Laskowski, M. “A Perspective on Blockchain Smart Contracts: Reducing Uncertainty and Com-
plexity in Value Exchange.” arXiv (2018) (accessed 29 October 2018) https://ssrn.com/abstract=
72 Atzei, N., Bartoletti, M., Cimoli, T. “A Survey of Attacks on Ethereum Smart Contracts (SoK).” In
M. Maffei, M. Ryan (Eds.), Principles of Security and Trust, 6th International Conference, POST 2017,
Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2017, Uppsala,
Sweden, April 22-29, 2017, Proceedings. Berlin: Springer 164–186 (2017) https://doi.org/10.1007/
73 Details available at https://www.iso.org/committee/6266604.html.
74 Lin, I.-C., Liao, T.-C. “A Survey of Blockchain Security Issues and Challenges.” International Journal of
Network Security 19.5 653–659 (2017) https://dx.doi.org/10.6633/IJNS.201709.19(5).01.
75 Conti, M., Lal, C., Ruj, S., Sandeep, K. E. “A Survey on Security and Privacy Issues of Bitcoin.” arXiv
(2017) (accessed 29 October 2018) https://arxiv.org/abs/1706.00916.
76 Crosby, M., Pattanayak, P., Verma, S., Kalyanaraman, V. “Blockchain Technology: Be-
yond Bitcoin.” Applied Innovation Institute (2016) (accessed 29 October 2018) https://www.
77 Harvey, C. R. “Cryptoﬁnance.” SSRN (2016) (accessed 29 October 2018) https://ssrn.com/
78 Meiklejohn, S., Orlandi, C. “Privacy-Enhancing Overlays in Bitcoin.” In M. Brenner, N. Christin, B. John-
son, K. Rohloff (Eds.), Financial Cryptography and Data Security, FC 2015 International Workshops, BIT-
COIN, WAHC, and Wearable, San Juan, Puerto Rico, January 30, 2015, Revised Selected Papers. Berlin:
Springer 127–141 (2015) https://doi.org/10.1007/978-3-662-48051-9_10.
79 Ben-Sasson, E., Chiesa, A., Tromer, E., Virza, M. “Scalable Zero Knowledge Via Cycles of Elliptic
Curves.” In J. A. Garay, R. Gennaro (Eds.), Advances in Cryptology–CRYPTO 2014. Berlin: Springer 276–
294 (2014) https://doi.org/10.1007/978-3-662-44381-1_16.
80 Tasca, P., Liu, S., Hayes, A. “The Evolution of the Bitcoin Economy: Extracting and Analyzing the
Network of Payment Relationships.” The Journal of Risk Finance 19.2 94–126 (2016) https://doi.org/
81 Ben-Sasson, E., et al. “Zerocash: Decentralized Anonymous Payments from Bitcoin.” In 2014 IEEE Sym-
posium on Security and Privacy. IEEE 459–474 (2014) https://doi.org/10.1109/SP.2014.36.
82 de Wit, J. “DAO, Can It Be Viable? An Exploratory Research on the Viability of a Blockchain Based
Decentralized Autonomous Organization.” Radboud University (2017) (Master’s Thesis) https://theses.
83 Hearn, M. “Corda: A distributed ledger.” (2016) (accessed 29 November 2018) https://www.r3.com/
36 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
84 No Author. “Mixing Service.” Bitcoin Wiki (accessed 1 January 2018) https://en.bitcoin.it/wiki/
85 No Author. “CoinJoin.” Bitcoin Wiki (accessed 1 June 2017) https://en.bitcoin.it/wiki/
86 Noether, S., Mackenzie, A., Monero Research Lab. “Ring Conﬁdential Transactions.” Ledger 11–18
oser, M., B¨
ohme, R. “Anonymous Alone? Measuring Bitcoin’s Second-Generation Anonymization
Techniques.” In 2017 IEEE European Symposium on Security and Privacy Workshops. IEEE 32–41 (2017)
88 Rivest, R., Shamir, A., Tauman, Y. “How to Leak a Secret.” In C. Boyd (Ed.), Advances in Cryptology—
ASIACRYPT 2001. Berlin: Springer (2001) https://doi.org/10.1007/3-540-45682-1_32.
89 Guegan, D. “Public Blockchain Versus Private Blockchain.” Universit´
eon-Sorbonne (Paris 1), Cen-
tre d’Economie de la Sorbonne working papers (2017) (accessed 29 October 2018) https://econpapers.
90 Maurer, F. K. “A Survey on Approaches to Anonymity in Bitcoin and Other Cryptocurrencies.” In
H. C. Mayr, M. Pinzger (Eds.), 46. Jahrestagung der Gesellschaft f¨
ur Informatik, Informatik 2016, 26-
30 September 2016, Klagenfurt, ¨
Osterreich. Bonn: Gesellschaft f ¨
ur Informatik 2145–2150 (2016) https:
91 Reid, F., Harrigan, M. “An Analysis of Anonymity in the Bitcoin System.” In A. Y., Y. Elovici, A. Cremers,
N. Aharony, A. Pentland (Eds.), Security and Privacy in Social Networks. New York: Springer 197–223
92 Timmerman, K., Thomas, M., et al. “Ethereum: More than ‘The New Bitcoin’.” The Proctor 37.5 26
oser, M., B¨
ohme, R. “Trends, Tips, Tolls: A Longitudinal Study of Bitcoin Transaction Fees.”
In M. Brenner, N. Christin, B. Johnson, K. Rohloff (Eds.), Financial Cryptography and Data Secu-
rity, FC 2015 International Workshops, BITCOIN, WAHC, and Wearable, San Juan, Puerto Rico, Jan-
uary 30, 2015, Revised Selected Papers. Berlin: Springer 19–33 (2015) https://doi.org/10.1007/
94 Carlsten, M. The Impact of Transaction Fees on Bitcoin Mining Strategies. (PhD Thesis) Princeton Univer-
95 No Author. “Developer Guide: Mining.” bitcoin.org (2015) (accessed 25 April 2017) https://bitcoin.
96 Alvseike, R., Iversen, G. A. G. “Blockchain and the Future of Money and Finance: A Qualitative Ex-
ploratory Study of Blockchain Technology and Implications for the Monetary and Financial System.” Nor-
wegian School of Economics (2017) (Master’s Thesis) http://hdl.handle.net/11250/2453330.
97 Scott, B. “How Can Cryptocurrency and Blockchain Technology play a Role in Building Social and Soli-
darity Finance?” United Nations Research Institute for Social Development, Working Paper (2016) (accessed
29 October 2018) http://www.unrisd.org/brett-scott.
98 Evans, C. “Bitcoin in Islamic Banking and Finance.” Journal of Islamic Banking and Finance 3.1 1–11
99 Quintana Diaz, J. M. “The Merger of Cryptography and Finance-Do Cryptographic Economic Systems
Lead to the Future of Money and Payments?” SSRN (2014) (accessed 29 October 2018) https://ssrn.
100 Boudguiga, A., et al. “Towards Better Availability and Accountability for IoT Updates by Means of a
Blockchain.” In IEEE Security & Privacy on the Blockchain (IEEE S&B 2017) an IEEE EuroS&P 2017 and
Eurocrypt 2017 Afﬁliated Workshop. (2017) https://doi.org/10.1109/EuroSPW.2017.50.
37 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
101 Dorri, A., Kanhere, S. S., Jurdak, R. “Towards an Optimized BlockChain for IoT.” In Proceedings of the
Second International Conference on Internet-of-Things Design and Implementation. New York: Association
for Computing Machinery 173–178 (2017) https://doi.org/10.1145/3054977.3055003.
102 Andrews, C., Broby, D., Paul, G., Whitﬁeld, I. “Utilising Financial Blockchain Technologies in Advanced
Manufacturing.” University of Strathclyde (2017) (accessed 29 October 2018) https://strathprints.
103 Mainelli, M., von Gunten, C. “Chain Of A Lifetime: How Blockchain Technology Might Transform
Personal Insurance.” Long Finance and Z/Yen Group (2014) (accessed 29 October 2018) http://archive.
104 Stellnberger, M. “Insurance Through Blockchain: A Hybrid Approach.” martin-
stellnberger.co (2016) (accessed 29 October 2018) https://www.martinstellnberger.co/s/
105 Badzar, A. “Blockchain for Securing Sustainable Transport Contracts and Supply Chain Transparency:
An Explorative Study of Blockchain Technology in Logistics.” Lund University (2016) (Master’s Thesis)
106 Hackius, N., Petersen, M. “Blockchain in Logistics and Supply Chain: Trick or Treat?” In W. Kersten,
T. Blecker, C. M. Ringle (Eds.), Proceedings of the Hamburg International Conference of Logistics (HICL).
Hamburg: epubli 3–18 (2017) https://doi.org/10.15480/882.1444.
107 Kotobi, K., Bil´
en, S. G. “Blockchain-Enabled Spectrum Access in Cognitive Radio Networks.” In
2017 Wireless Telecommunications Symposium. IEEE 1–6 (2017) https://doi.org/10.1109/WTS.2017.
108 Plant, L., et al. “Implications of Open Source Blockchain for Increasing Efﬁciency and Transparency of the
Digital Content Supply Chain in the Australian Telecommunications and Media Industry.” Australian Jour-
nal of Telecommunications and the Digital Economy 5.3 15 (2017) https://doi.org/10.18080/ajtde.
109 Chakravorty, A., Rong, C. “Ushare: User Controlled Social Media Based on Blockchain.” In Proceed-
ings of the 11th International Conference on Ubiquitous Information Management and Communication.
New York: Association for Computing Machinery 99 (2017) http://dx.doi.org/10.1145/3022227.
110 Kuo, T.-T., Kim, H.-E., Ohno-Machado, L. “Blockchain Distributed Ledger Technologies for Biomedical
and Health Care Applications.” Journal of the American Medical Informatics Association 24.6 1211–1220
38 ISSN 2379-5980 (online)
LEDGER VOL 4 (2019) 1-39
Appendix N: Blockchains Analysed for the Taxonomy
Bitcoin Forerunner and by far the most widely used cryptocurrency.
Privacy-centric digital currency. The digital currency Dash has different functionalities e.g., instant transactions.
Dash is based on the Bitcoin source code, but it allows anonymity while performing transactions.
Monero is an open-source digital currency, with the focus on decentralisation, scalability and privacy. It is based
on the CryptoNote protocol, which has an enabled anonymous layer.
LiteCoin is a P2P cryptocurrency and open source software project. The Litecoin is technically nearly identical to
Bitcoin, except for the proof-of-work cryptographic function used.
Zcash is a decentralised and open-source cryptocurrency, which combines privacy with selective transparency of
Peercoin Cost-effective and sustainable cryptocurrency based on proof-of-stake.
A concept that allows attaching metadata to Bitcoin transactions and leveraging the Bitcoin infrastructure for
issuing and trading immutable digital assets that can represent real world value.
A meta-protocol layer that enables new digital currencies, digital assets, and communication protocol to existing
on top of the Bitcoin blockchain.
NameCoin is an asset registry on top of the Bitcoin Blockchain, which enables a decentralised domain name
Counterparty enables anyone to write speciﬁc digital agreements or programs known as smart contracts, and
execute them on the Bitcoin blockchain.
NXT is a safe, transparent and decentralised system for sharing data and allowing payments to people all over the
world. Ardor platforms enable smart contract functions with NXT.
Ethereum Ethereum is an open-source platform to build blockchain-based applications in different business ﬁelds.
Monax is an open platform for developers and DevOps to build, ship, and run blockchain-based applications for
business ecosystems. Monax is known as a private blockchain offered by the monax company.
Cosmos is an architecture for cross-chain interoperability where independent blockchains can interact via an
inter-blockchain communication (IBC) protocol, a kind of virtual UDP or TCP for blockchains each driven by the
Byzantine fault tolerant (BFT) consensus algorithm, similar to Tendermint.
COMIT is a cryptographically-secure off-chain multi-asset instant transaction network (COMIT) that can connect
and exchange any asset on any blockchain to any other blockchain using a COMIT cross-chain routing protocol
Synerio Synerio is intended to become a decentralised social network.
Ripple Ripple is a global real-time ﬁnancial settlement provider.
Stellar Stellar is a decentralised multicurrency-exchange platform for people without access to the banking system.
Hyperledger (or the Hyperledger project) is an open source blockchain platform, started in December 2015 by the
Linux Foundation, to support blockchain-based distributed ledgers. It is focused on ledgers designed to support
global business transactions, including major technological, ﬁnancial, and supply chain companies, with the goal
of improving many aspects of performance and reliability.
Tendermint is a high-performance blockchain consensus engine that enables to run Byzantine fault tolerant
applications written in any programming language. Tendermint is a partially synchronous BFT consensus protocol
derived from the DLS consensus algorithm.
Corda Open-source distributed ledger platform.
2-3 Enigma Distributed ledger technology, created by the MIT digital currency lab.
Table 1. Blockchain Technologies
39 SSN 2379-5980 (online)