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A Blockchain Model for Fair Data Sharing in Deregulated Smart Grids

Authors:
  • Edo State University Iyamho

Abstract and Figures

The emergence of smart homes appliances has generated a high volume of data on smart meters belonging to different customers which, however, can not share their data in deregulated smart grids due to privacy concern. Although, these data are important for the service provider in order to provide an efficient service. To encourage customers participation, this paper proposes an access control mechanism by fairly compensating customers for their participation in data sharing via blockchain and the concept of differential privacy. We addressed the computational issues of existing ethereum blockchain by proposing a proof of authority consensus protocol through the Pagerank mechanism in order to derive the reputation scores. Experimental results show the efficiency of the proposed model to minimize privacy risk, maximize aggregator profit. In addition, gas consumption, as well as the cost of the computational resources, is reduced. Index Terms-Blockchain, consensus mechanism, proof of authority, privacy preserving and smart grid. I. INTRODUCTION Presently, because of the rapid growth of the world population and the technological innovations, a lot of energy is needed in a short period of time and during peak hours, and its effect increases the cost of production. Customers can, therefore, optimize their utilization based on the current energy demand and supply. As a result, demand response and dynamic pricing proposal are subject to privacy issues. In a smart grid, customers will share their hourly information load profile with a service provider only to allow a certain level of privacy to be maintained, which is a major barrier for customer participation. In order to efficiently aggregate customer data, while preserving their privacy, Liu et al. [1] propose a privacy-preserving mechanism for data aggregation. The proposed solution minimizes the cost of communication and computational overhead. However, a trusted environment is not considered. To achieve a trusted environment, several studies in [2]-[8] used blockchain as privacy-preserving mechanism for data aggregation; privacy protection and energy storage; secure classification of multiple data; incentive announcement network for smart vehicle; crowdsensing applications; dynamic tariff decision and payment mechanism for vehicle-to-grid. A survey concerning privacy protection using blockchain is discussed in [9]. The survey highlights all the existing
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A Blockchain Model for Fair Data Sharing in
Deregulated Smart Grids
Omaji Samuel1, Nadeem Javaid1,, Muhammad Awais1, Zeeshan Ahmed2, Muhammad Imran3, Mohsen Guizani4
1Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
2Riphah Institute of System Engineering, Riphah International University, Islamabad
3College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
4Department of Electrical and Computer Engineering, University of Idaho, Moscow, ID 83844, USA
Corresponding author: nadeemjavaidqau@gmail.com
Abstract—The emergence of smart homes appliances has
generated a high volume of data on smart meters belonging to
different customers which, however, can not share their data in
deregulated smart grids due to privacy concern. Although, these
data are important for the service provider in order to provide
an efficient service. To encourage customers participation, this
paper proposes an access control mechanism by fairly com-
pensating customers for their participation in data sharing via
blockchain and the concept of differential privacy. We addressed
the computational issues of existing ethereum blockchain by
proposing a proof of authority consensus protocol through the
Pagerank mechanism in order to derive the reputation scores.
Experimental results show the efficiency of the proposed model
to minimize privacy risk, maximize aggregator profit. In addition,
gas consumption, as well as the cost of the computational
resources, is reduced.
Index Terms—Blockchain, consensus mechanism, proof of
authority, privacy preserving and smart grid.
I. INTRODUCTION
Presently, because of the rapid growth of the world pop-
ulation and the technological innovations, a lot of energy
is needed in a short period of time and during peak hours,
and its effect increases the cost of production. Customers
can, therefore, optimize their utilization based on the current
energy demand and supply. As a result, demand response and
dynamic pricing proposal are subject to privacy issues. In a
smart grid, customers will share their hourly information load
profile with a service provider only to allow a certain level of
privacy to be maintained, which is a major barrier for customer
participation.
In order to efficiently aggregate customer data, while
preserving their privacy, Liu et al. [1] propose a privacy-
preserving mechanism for data aggregation. The proposed so-
lution minimizes the cost of communication and computational
overhead. However, a trusted environment is not considered.
To achieve a trusted environment, several studies in [2]–[8]
used blockchain as privacy-preserving mechanism for data
aggregation; privacy protection and energy storage; secure
classification of multiple data; incentive announcement net-
work for smart vehicle; crowdsensing applications; dynamic
tariff decision and payment mechanism for vehicle-to-grid.
A survey concerning privacy protection using the blockchain
is discussed in [9]. The survey highlights all the existing
encryption mechanisms; however, information leakage and
access control mechanism are not discussed. Yassine et al. [10]
solve the access control problem by fairly compensating
consumers for participating in the energy market via differ-
ential privacy (DP). Based on their mechanism, privacy risk
values can be derived from the activity of daily living (ADL)
categorization of consumers. Test results confirm that, as the
privacy level increases, consumer participation reduces as well.
However, the computational burden for each transaction is
incurred. More so, there is absent of a secure and trusted
environment for negotiation, thus, the data analyst may collide
with the service provider. Work in [11] addresses the trust
and transactional privacy problem through the incorporation
of the blockchain. Here, zero-knowledge proof encryption is
proposed to enhance contractive interaction among parties.
In the proposed scheme, privacy is achieved via independent
privacy, posterior privacy and financial fairness, furthermore,
defaulters are penalized and non-defaulters are compensated.
Despite the fact that the customers are made anonymous, i.e.,
they are identified by their addresses and identity (ID) in the
blockchain, however, de-anonymizing consumers are possible
by linking transactions and keys. In order to address the issue,
Knirsch et al. [7] propose a privacy-preserving blockchain
based on a dynamic tariff. For privacy, the honest-but-curious
adversary model is proposed. In addition, the authors claimed
that ID of a customer should be changed by generating a new
key pair for each transaction to avoid information leakage.
However, generating a new key pair for each changed ID may
result in traceability issue during an audit.
From the literature above, the authors focus on encryption
and anonymization methods, while the possible cause of
information leakage is not completely resolved. This paper
further identifies this additional cause. For instance, a house-
hold electronic company will want to know the reliability
of their products. Such a company needs access to the load
consumption profile of household customers. This will enable
them to improve their services, perform dynamic billing,
issue insurance deals, and make compelling campaigns that
can rapidly and with ease deliver fast and efficient services
to their customers [10]. On the other hand, the customers
do not wish to participate in data sharing of their private
data for privacy purposes which, take place in a non-trusted
environment. The current issue is used to derive our problem
statement. To encourage customers’ participation, an incentive
mechanism is needed for fair data sharing. This motivated us to
propose a trusted and decentralized data sharing environment
for customers while preserving their privacy. A blockchain
smart contract is designed to define the set of rules for data
access control and compensation against their privacy risk.
The proposed model has the following contributions:
1) We propose an efficient proof of authority consensus
(PoA) mechanism which, provides computational and
gas consumption cost minimization.
2) Decentralization: the proposed model is reliable as it
does not rely on a central server and all calculations are
made in a decentralized manner via blockchain nodes.
3) Transparency and verifiability: in the proposed model,
customers are selected as the authorized node based on
their reputation scores via the page rank mechanism.
4) Data integrity: since centralized approaches do not al-
low request verifiability through hashing mechanism
as blockchain does. Thus, the block chain approaches
ensure data integrity.
5) Privacy risk minimization for customers and aggregator
profit maximization for fair data sharing are achieved.
The organization of the paper is as follows: Section I pro-
vides the introduction, related work and paper contributions.
Section II discusses the proposed system model and problem
formulations. Experimental results are discussed in Section III
and Section IV provides the conclusion and future work.
Aggregator
Memory
Pool
Account
pool
Aggregator
Memory
Pool
Account
pool
1
Service provider
Access request from
the service provider
3Aggregator categorizes
customers’ information
based on ADL
5
Aggregator calculates the privacy risk level
2Service provider sent
offer with privacy
parameter and price of
offer
4
Aggregator accepts or rejects offer
If rejected, deal is terminated
6
If accepted, announces incentives in exchange
for allowing access to customer’s information
7
8Service provider makes payment
9Aggregator sent payment confirmation
10 Aggregator sent reward to the concerned
customer
11 Customer decides to opt-in or opt-out
ADL; Activity of daily living
Smart Contract
Two way flow
One way flow
Blockchain
Customer 200 Customer 300
Customer 500
Customer 1
...
...
...
Fig. 1: Proposed system.
II. PRO PO SE D SY ST EM M OD EL A ND P ROB LE M
FORMULATIONS
This section discusses the proposed system model and the
problem formulations.
A. System overview
Fig. 1 presents the blockchain system in a smart community
(SC) which comprises of customers, the aggregator and the
service provider. Customers are entities that buy goods or
services from the service provider. The service provider allows
its customers access to their services. The aggregator serves
as a broker between the customers and the service provider.
The aggregator receives a data access request from the service
provider, depending on the need and the cardinality of the
dataset, broadcast the privacy risk and the reward to the
customers. Then, the customers decide to opt-in or opt-out
of the deal. Once the deal is sealed, the aggregator protects
the data at an agreed level of privacy and permit data access
to the service provider [10].
B. Blockchain network
The blockchain is an efficient mechanism designed for
storing transaction record and distributed data for participants
without any intervention of a trusted third party. Blockchain
operates as a public system, where transaction verification
is done by an independent group of nodes. In addition,
blockchain can be a permissioned system, i.e., transaction
processing and the addition of a new block are carried out
by only authorized individuals. The blockchain is built on a
continuous sequence of blocks, such that, each block has a
hash value chained by a previous block. Several consensus
protocols have been proposed to prevent fraudulent transac-
tions in the blockchain. This paper considers some of these
consensus protocols. The proof of work (PoW) depends on
high computing resources to generate the hash value less than
or equal to the present target value of the block. A nonce
is added to the block via brute-force search, thus, the first
winner can link his chain to the next block. In addition, PoW
prevents internal and external attacks, however, it promotes
centralization due to the reliance on an application-specific
integrated circuit. The proof of stake approach depends on
the node with the highest stakes (coins). The node having the
most coins is given the least difficulty to add the nonce that
generates the next block. However, there are risks of “fork”,
and it promotes centralization by favoring node with the most
coins. The PoA approach depends on attributes as a stake, only
the voted node can add the chain to the block.
C. Aggregator
In SC, the aggregator operates a monopolistic energy mar-
ket. The functions of the aggregator are multi-fold: Firstly, ag-
gregator coordinates the operations and behaviors of multiple
customers. Because of storage and computational constraints,
the customers store an index of their metadata for decreasing
system cost. Secondly, The aggregator receives records of the
local transaction which are encrypted and structured into a
block after being authorized and audited by all customers
during the consensus process. Every block has a cryptographic
hash which is used for verification and traceability. Blocks are
added in temporal order for each transaction which is visible
to all customers.
Lastly, we envisage that aggregator acts on behalf of the
customers and partake in the negotiation with the service
provider as shown in Fig. 1. In this research, we assume the
customer is aware of the data market and his willingness to
share his private information, in order to get compensated for
his privacy loss. Aggregator uses the concept of ADL to derive
an analogy of the categories of activities from the customer’s
load consumption. This will enable both the customer and
aggregator to identify which is the potential privacy risk
depending on the sensitivity of the private information. The
categorized data will be used to define the privacy risk matrix
taking into consideration the usage of data against the potential
risks to privacy. We propose a mechanism that provides
privacy-preserving and also computes the privacy risk value
that will be used to determine the customer’s reward.
D. Proof of authority consensus mechanism for blockchain
PoA is a part of the permissioned-blockchain consensus
mechanism which is famously based on high performance with
the typical Byzantine fault tolerance algorithm. PoA belongs
to the ethereum ecosystem for private networks [12]. PoA
algorithms depend on a set of Kauthorities also known as
trusted nodes. Each trusted node has a unique ID and is
said to exhibit honesty, i.e., K
2+ 1 [12]. For any ordered
transaction by a non-authorized node, the authorized node
executes a consensus. The PoA algorithm relies on a mining
schema that enables a fair distribution of authorities for block
creation among trusted nodes. A reputation mechanism is
proposed in this work for the honest distribution of authority
to trusted nodes based on their reputation scores. A Pagerank
algorithm is used for the implementation. Pagerank algorithm
was originally described by Lawrence Page and Sergey Brin,
co-founders of Google in several publications [13]. Pagerank
algorithm determines the importance of the web, not entirely
the web, but also individual pages. The page rank of any
address (A) is recursively defined by the page ranks of other
addresses linked to Aas shown in Fig. 2. Initially, equal
probability of 1
N(where Nis the number of nodes) is given
to all addresses as their first reputation scores, thereafter,
reputation scores are updated which is mathematically defined
as [13].
A B
C
DE
A B
C
DE
AB
C
DE
AB
C
DE
A B
C
DE
AB
C
DE
1. Start with the set of addresses
(A-E)
2. Crawl the address to determine
the link structure
3. Assign each address an
initial rank using 1/N
4. Update the rank of each
address by using Eq (1)
A B
C
DE
AB
C
DE
1. Start with the set of addresses
(A-E)
2. Crawl the address to determine
the link structure
3. Assign each address an
initial rank using 1/N
4. Update the rank of each
address by using Eq (1)
AB
C
DE
AB
C
DE
A:0.2 B:0.2
C:0.2
D:0.2E:0.2
Fig. 2: Showing the working of Pagerank algorithm.
P R(A) = (1 d) + d(P R(B1)
C(B1)+· · · +P R(Bn)
C(Bn)),(1)
where P R(A)is the page rank of A,P R(Bn)is the page rank
of Bthat is linked to A,C(Bn)is the number of outbound
links on address Bnand dis damping factor such that 0
d1. Note that the P R(Bn)linked to an address Adoes not
influence the page ranking of A, uniformly. With the Pagerank
algorithm, the page rank of an address Bnis weighted by the
amount of outbound linked C(Bn)on the address B[13].
This implies that more the outbound links A has to B, the
smaller will be the page rank of A. In addition, the weighted
page rank of address Bnis then summed up. The resultant
effect of an additional inbound link for address Awill always
increase address A’s page rank [13]. Overall, the sum of the
weighted ranks of all addresses is multiplied with a damping
factor. Fig. 3 describes the proposed PoA in solidity. Line 4-9
computes the level of difficulty that is used in the consensus.
While line 10-15 calculates the time frame for performing the
consensus. We consider the time span in seconds for a week
with a target spacing of 600s. This provides enough time for
the verification and validation of the transaction. Line 16-34
performs the consensus, here, we consider the hashes of the
customer block to calculate the level of difficulty based on the
customer’s reputation score. We calculate the computational
cost of the customer at line 25, while line 35-40 returns the
status of state and verification for each customer. We simplify
the description by using the Algorithm (1).
1. uint public d ifficulty__PoA;
2. string public state__PoA="Idle";
3. bool public verfiyPoA;
4. function calc _difficulty_PoA() public{
5. difficulty_ _PoA = 10000; // 429496 7295
6. bytes32 prev
=bytes32(keccak25 6(abi.encodePacked(blockha sh(block.number))));
7. if (prev_ha sh == 0){
8. difficulty_ _PoA = block.difficulty;
9. }
10. uint TargetS pacing = 10 * 60;
11. uint TargetTimespan = 7 * 24 * 60 * 60;
12. uint Interval = TargetTimespa n / TargetSpacing;
13. uint ActualSp acing = now - block.timesta mp;
14. difficulty__PoA = difficul ty__PoA * ((Interval - 1) * TargetSpacing
+ 2 * ActualSpacing) / ((TargetSpacing + 1) * Ta rgetSpacing);
15. }
16. function make_consensus_PoA(ad dress sender) public{
17. calc_difficulty_P oA();
18. state__PoA = "Busy";
19. uint time_period = now - b lock.timestamp;
20. if (time_period > 3600 * 1000) {
21. state__PoA="consensus fai led";
22. }
23. uint Reputation = reputati on ;
24. bytes32 target=bytes32(keccak2 56(abi.encodePacked(difficulty __PoA
*Reputation)));
25. POA=max(target)/ target;
26. bytes32 Prev_hash =
bytes32(keccak256(ab i.encodePacked(blockhash(bl ock.number))));
27. if (Prev_hash < target ) {
28. state__PoA="consensus complete d";
29. verfiyPoA=true;
30. } else {
31. state__Po A="consensus failed";
32. verfiyPoA=false;
33. }
34. }
35. function getConsensus_poa( )public view returns(string memory){
36. return state__PoA;
37. }
38. function getVerify_poa() public view returns(bool){
39. return verfi yPoA;
40. }
Fig. 3: Proposed PoA.
E. Security requirements and analysis of the proposed system
The proposed blockchain fine-grained access control for
fair data sharing meets the fundamental security requirements:
single registration, authentication, fine-grained access and cus-
tomer anonymity. Furthermore, the security analysis of the
Algorithm 1 Smart contract
1: procedure SMART C ON TR ACT t: transaction
2: Intialized algorithm parameters
3: set StateI dle”;
4: P ubkey keyi;customers public keys
5: if t.key P ubkey then
6: if State == “idlethen
7: Get customers reputation
scores using Eq (1);
8: Make consensus;
9: else if V erif yP oA == true then
10: set State = “Consensus completed”;
11: Check if there is request!;
12: Service provider makes privacy
offer;
13: if IsRequest then
14: Verify the service provider
request;
15: Calculate the privacy
revealing using Eq (9);
16: Calculate the privacy risk
using Eq (10);
17: if IsOff er then
18: Accept deal;
19: Calculate aggregator profit;
20: Service provider makes
payment;
21: else Reject deal;
22: if IsReward then
23: Customer opt-in;
24: else Customer opt-out;
25: else set S tate = “Busy;
proposed system is described based on the selected type of
attacks.
1) Similarity attacks: there might be a malicious actor
trying to exploit the weakness of the proposed protocol. Thus,
any system that is safe against arbitrary faults will be safe
against random malicious actors. We assume a situation where
a malicious actor collides with other nodes and generates fake
link structures, alternatively, the page ranking mechanism may
generate page ratings of two nodes with the same value. To
prevent similarity between two nodes, the P R in Eq (1) is
modified as:
P R(A) =
N
X
j=1
Hp(A)P R(A),(2)
where Nis the number of authorized nodes and Hp(A)is the
historical performance of address (A) which is given as:
Hp(A) = (1,if P R(A)is the highest
0, otherwise,(3)
Thus, the reward for each address can serve as an incentive
or disincentive and it is calculated as:
R=P R(A)
PN
j=1 P R .(4)
2) Double spending attacks: the default ethereum
blockchain utilizes the PoW protocol which is known for
computational resource wastage [14]. The proposed PoA
eliminates and saves wastage of computing resources by
limiting the number of competitors, thus, the negative impacts
of competitive nodes are removed.
3) Birthday collision resilience: in the proposed PoA pro-
tocol, there are no risks of “fork”, which effectively prevents
the likelihood of two nodes having the same reputation scores.
F. Characteristics of the proposed PoA
In this section, the properties of the proposed PoA protocol
is analyzed based on consistency, availability and partition
tolerance.
1) Consistency: a blockchain is said to exhibit consistency
if there are no “forks” [12]. “Forks” can be eliminated by
increasing the level of difficulty or reducing the number of
competitors. When a fork can not be resolved, then we say
consistency cannot be attained. Our proposed protocol reduces
the number of competitors, thereby, exhibiting consistency.
2) Availability: a blockchain is said to be available if
transactions are committed and served [12]. In our pro-
posed scheme, the transactions are permanently stored in the
blockchain, therefore, making the transactions immutable.
3) Partition tolerance: in the case where two or more au-
thorized nodes have the same reputation scores, there is a need
to create a partition. Thus, the authorities are grouped into two
disjoint classes such that each class cannot communicate with
each other [12]. To achieve partition tolerance, we consider
the timeliness of their interaction by using the power-law
distribution as given below.
P=θ1(ttj
i)θ2,(5)
where θ1and θ2are constant parameters that denote the
impacts of interaction, trepresents the current time and tj
i
denotes the time slots when the reputation score was created
for the authorized node jand i, respectively.
G. Access control of customer data in deregulated SC
In SC, the aggregator categorized customers’ energy con-
sumption according to ADL. In home energy management,
ADL is derived from the smart meter data about customer’s
usage of appliances. The ADL is mostly used to measure and
classify activities mainly inside a house and these categories
are discussed in [10]. By combining several load categories
from different appliances, the aggregator can easily infer
about the way customer behaves. This will eventually lead
to privacy disclosure. Let U1, U2, . . . , Unbe the categories of
data based on the different levels of ADL. Each level has its
own implication of privacy breach. More so, data in different
categories cannot replace one another, revealing any of them
increases the privacy risk. Let the number of categories in a
set be GnUn, then, we perform normalization on the data
which is defined in Eq (6). Gnrepresents the ratio of categories
in customer privacy.
Gn=Gnmin(Gn)
max(Gn)min(Gn).(6)
In this work, we assume the privacy risk value denoted as Rv al
i,n
for each customer iof category type n. Thus, the weighted
privacy risk is calculated as [10]:
ωn= GnRval
i,n ,(7)
ωn=ωn
Pn
g=1 ωg
.(8)
We modified Eq (7) to include the privacy risk revealing value
ηnas defined in Eq (9) [10].
ωn=ηn
Gn
Gn.(9)
The ωnprovides a reward value that a customer will get
after considering the privacy risks involved. Ideally, we intend
to get more ωnthat will allow the higher revelation of the
customer personal data. In this way, the reward given to the
customer is logical, notwithstanding the implications linked to
their privacy.
In order to analyze the customers’ decision with respect
to opting-in or opting-out, and to allow access to their private
data, we model the customer decision based on preference and
the level of reward that customer will get against their privacy
risks. Three groups of customer preferences are discussed
in [10]. Firstly, some customers are concerned about a little
reward for allowing access to their private data. Secondly,
customers expect rewards proportional to their privacy risk for
revealing their private data, and lastly, customers who only
care about their privacy risks. Let’s denote the customers’
preferences as Πsuch that Π[0,1]. When Π=1, model the
customer who intends to maximize their privacy and Π=0,
model the customer who completely relinquished his privacy
in exchange for a reward. Hence, we define the privacy risk
Rval
i,n of a customer iof type nover the private data Dp[10].
Rval
i,n (Dp) = P C(Dp).S L(Dp),(10)
where the privacy concern PC (Dp)∈ {0,1}and sensitivity
level SL(Dp)∈ {0,1}. The e-DP is defined as [10]:
Qt=1
2bexp(|Gn|
b),(11)
where b=qσ2
2is the diversity and σ2is the variance of
the Laplacian distribution. Using the e-DP, the SL(Dp)is
obtained by finding their differences (f(G1)f(G2)), i.e.,
the set G1and G2differing on at most one element [10].
III. SIMULATION RESULTS
In this section, the experimental settings are presented,
then the results derived from the computation are described.
Blockchain performances in terms of gas consumption and
computational cost of the proposed PoA with that of existing
PoW consensus and privacy-preserving are discussed. The
dataset for the simulation is taken from [10].
A. Experimental setup
We developed our blockchain using the ethereum plat-
form [15] with the following dependencies; Truffle v5.0.8
(core: 5.0.8), Solidity v0.5.0 (solc-js), Node v10.13.0 and
Web3.js v1.0.0-beta.37. In addition, we customized our codes
using JavaScript. The hash operations are performed using
the solidity keccak256 library and some of the data used
are randomly generated, if not specified. To evaluate the
performance of our proposed PoA, we used 500 peers, this
number is chosen for resilience and scalability purposes. Other
simulation results are generated using MATLAB2018. The
hardware platform was a Dell i5, with 8 GB RAM and CPU
of 1.60Hz and 1.80GHz.
B. Performance comparison between proposed PoA and PoW
We consider the complexity of computation as the computa-
tional cost related to the cost of the sharing. The computation
of gas corresponds to the minimum operating level of ethereum
virtual machine (EVM), where each opcode is associated with
a gas cost. To compute the gas cost of any two quantities, it
is derived from the gas operator. For examples; ADD uses 3
gases, MUL uses 5 gases, SU B uses 3 gases, DIV uses 5
gases, LT and GT use 3 gas each, and EQ uses 3 gases [15].
Note, we set the gas limit to 6721975 as the base even when
no interaction is done with the contract. In the EVM, we
model the gas consumption with regard to the number of
peers for each network as seen in Fig. 4a. Although, we do
not get the exact cost of gas for a transaction, even before
completion as the transaction in the same block may distort
the result. However, we provide a sufficient estimate based on
the algorithm complexity. Results report that the PoW incur
more gas as compared to our proposed PoA. The reason is due
to the PoW algorithm complexity. As the number of peers in
the network increases, the overall performance is undermined
by the total cost of the transaction.
In Fig. 4b, the height of a block refers to the number of
blocks in the blockchain. The computational cost is the number
of hash operations that the authorized block tried and it is
being evaluated by hashes. The PoW requires that the miner
shows proof of their work. Here, a mathematical puzzle, which
is not easily solved by the miner, however, validation is done
easily.
Although, it requires extra computational resources to solve
this puzzle. For a single customer, the expected computing
resources should be lower, since only the authorized customer
(block) is allowed to add a block after being evaluated by their
reputation scores. We formulate the required computational
resources of PoW as max(target)
target ×232 [16] and the proposed
100 150 200 250 300 350 400 450 500
Peer number
0
0.5
1
1.5
2
2.5
3
3.5
4
Gas consumption
108
PoW
Proposed PoA
(a) Comparison of gas consumption.
0 5 10 15 20 25 30 35 40
Height of block
0
0.5
1
1.5
2
2.5
3
3.5
Computational cost (Hashes)
1010
PoW
Proposed PoA
(b) Comparison of computational cost.
0.1 0.2 0.3 0.4 0.5 0.6
Parameter b
0
1
2
3
4
5
6
7
8
9
Number of customers
Number of customers for = 0.1 [10]
Number of customers for = 0.2 [10]
Number of customers for = 0.1 (proposed)
Number of customers for = 0.2 (proposed)
(c) Effect of parameter “b” on the number of customers.
(d) Effect of parameter “a” on the number
of customers.
0.1 0.2 0.3 0.4 0.5 0.6
Privacy level
0
2000
4000
6000
8000
10000
12000
Aggregator profit (cents)
(e) Privacy level versus aggregator profit.
0.1 0.2 0.3 0.4 0.5 0.6
Privacy level
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Risk revealing
(f) Risk revealing versus privacy level.
Fig. 4: Showing results of different cases.
PoA as max(tar get)
P R(A)×target ×232 . For comparison, we consider 40
blocks. From the results, our proposed PoA continues to mine
efficiently in different blocks, which show a cost reduction
in the computational resources. The PoW results show that
different block behaves differently, because of their compu-
tational resources and the ability to solve the mathematical
puzzle. During the experiment, we have issues of orphan
blocks. Orphan blocks occur when two winner miners publish
a block at the same time. Although, this situation is peculiar
to all forms of blockchain. If an orphan block is mined, it will
have the same block number, however, different transaction
ID than the others. To prevent miners from creating orphan
blocks, we increase the level of difficulty, which eventually
provides more security.
C. Access control via differential privacy
In this section, we present the discussion of privacy-
preserving to enable access control to the private information
of customers. We categorized 100 customers based on their
ADL for two days and for each customer, a privacy risk
Rval
i,n (Dp)is assigned, which lies between 0 and 1, i.e.,
uniformly distributed. To derive the number of customers
that will opt-in and opt-out, the number Nis formulated as
a..Qt.cn,t +b.r [10]. The privacy cost cn,t takes the range
from 1 and 5 cents, the choice of reflects the probability
of privacy breach and we fixed the values to be 0.1 and 0.2.
Where “a” denotes the customers that valued their privacy
more than the reward they get, while “b” denotes the customers
that valued the reward they get against their privacy concern
and Qtis the e-DP obtained from the Eq (11). In addition,
customers who want to participate in the blockchain are given
a 50% incentive.
In this experiment, we take the parameter “a” to be 1, 2,
3, 4, 5 and 6, while parameter “b” to be 0.1, 0.2, 0.3, 0.4
and 0.6. The objective of using these parameters is to analyze
the customers’ privacy attitude within the fixed values of .
From Fig. 4c, customers whose privacy are not important as
their concerned are the rewards, release their data without
the needful privacy. Simulation results confirm that with the
inclusion of blockchain, the privacy level is improved by at
least 50%. Thus, the number of customers whose rewards
are important than their privacy is further reduced by the
blockchain, since the blockchain provides security and as well
as privacy.
Fig. 4d illustrates the effect of parameter “a” on the number
of customers. The results show that customers who consider
their privacy more important than the reward they get by
granting access to their data. Report from Fig. 4d confirms that
with the involvement of blockchain technology, the number
of customers reduces as the parameter “a” increases by 50%.
From both Fig. 4c and Fig. 4d, the attitude of customers toward
their privacy concern has an effect on the market data sharing.
Fig. 4e shows the privacy level versus aggregator profit. We
derive the aggregator profit by using the formulation
r)(a..Qt.cn,t +b.r)[10]. Where Υis the privacy cost
from the service provider, which is assumed to be 13, 12, 11,
10, 9 and 8 cents, respectively. While is the cost incurred
by the aggregator for the appliance’s categorization and given
a fixed price of 10 cents. The results of Fig.4e illustrate that
as the value of increases, the aggregator profit decreases. As
a consequence, the aggregator can no longer incentivize the
customers. In addition, the customer whose interest is on the
reward will invariably opt-out from participation. At this point,
the aggregator is unable to manage the privacy purchasing
cost from the service provider and the cost of categorization.
However, by including blockchain, both the aggregator and
customers are rest assure of privacy risk minimization.
Fig. 4f shows the risk revealing versus privacy level. The
privacy revealing Rval
i,n of a customer iof category type n
lies in the range of 0 and 1. From the results, as the privacy
level increases, the Rval
i,n decreases as well. This confirmed
that blockchain provides a secure environment for data sharing
without the involvement of a third party. However, due to
private leakage, the proposed access control is important to
sanitize how data is shared via the incentive mechanism.
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Number of query
4
4.2
4.4
4.6
4.8
5
5.2
5.4
Average data
DP for original data
DP for original data with one added value
Fig. 5: Original data versus original data with added noise.
The Fig. 5 shows the DP of the original data versus DP of
the original data with added noise. In the proposed privacy-
preserving, the random noise is analyzed by using Eq (11).
Report from Fig. 5 confirms that the original data is close
to the data with the added noise. To further validate the
importance of the added noise, we run the experiment 6 times
and take the average results. As the number of query increases,
the more closeness is observed between the original data and
the data with noise.
IV. CONCLUSION
In this paper, a decentralized data sharing in the deregulated
smart grid is proposed. The customers’ privacy risk values are
identified via ADL. The issue of privacy cost based on privacy
risk is addressed via negotiation. The notion of differential pri-
vacy provides anonymity as a measure to conceal information
in order to achieve a minimum leakage. While, the blockchain
provides the decentralization, integrity, immutability, trusted
and secure environment for fair negotiations. The simulation
results confirm the importance of blockchain on the customers’
attitude towards privacy. Due to the computational issues in
the existing ethereum blockchain, we proposed a new PoA
in our model, where a customer is assigned a reputation
score obtained by using PageRank mechanism. The customer
with the highest reputation score is given the authority to
add a block in blockchain rather than solving the high
computational mathematical puzzle. Furthermore, we perform
security analysis on the proposed PoA by considering some
selected attacks such as similarity attacks, double spending
attacks and birthday collision resilience. The analysis confirms
that the proposed PoA addresses these attacks. More so, the
characteristics of the proposed PoA were examined based on
consistency, availability and partition tolerance.
In the future, we intend to consider the initial state as the
possible privacy breach, such that even if the customer has the
exact knowledge about the initial state of other customers, it
will be difficult to breach their privacy. In addition, we intend
to exploit other possibilities of weakness in the proposed
scheme and provide their solutions.
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