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Electric Vehicles Privacy Preserving using Blockchain in Smart Community

  • Edo State University Iyamho

Abstract and Figures

During the process of charging, electric vehicle's location is usually revealed when making payment. This brings about the potential risk to privacy of electric vehicle. We observe that the trade information recorded on blockchain may raise privacy concern and therefore, we propose a blockchain oriented approach to resolve the privacy issue without restricting trading activities through (ε, δ)-differential privacy. The proposed scheme does not only preserve the electric vehicle's location; however, prevents semantic, linking and data mining based attacks. Simulation results show that as the privacy level increases, the risk revealing decreases as well.
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Electric Vehicles Privacy Preserving
Using Blockchain in Smart Community
Omaji Samuel1, Nadeem Javaid1(B
), Faisal Shehzad1,
Muhammad Sohaib Iftikhar1, Muhammad Zohaib Iftikhar1, Hassan Farooq1,
and Muhammad Ramzan2,3
1Department of Computer Science, COMSATS University,
Islamabad 44000, Pakistan,
2Department of Computer Science and IT,
University of Sargodha, Sargodha, Pakistan
3Pakistan School of Systems & Technology,
University of Management and Technology, Lahore, Pakistan
Abstract. During the process of charging, electric vehicle’s location is
usually revealed when making payment. This brings about the potential
risk to privacy of electric vehicle. We observe that the trade informa-
tion recorded on blockchain may raise privacy concern and therefore, we
propose a blockchain oriented approach to resolve the privacy issue with-
out restricting trading activities through (, δ)-differential privacy. The
proposed scheme does not only preserve the electric vehicle’s location;
however, prevents semantic, linking and data mining based attacks. Sim-
ulation results show that as the privacy level increases, the risk revealing
decreases as well.
Keywords: Blockchain ·Demand side management ·Electric vehicle ·
Energy trading and privacy preserving
1 Introduction
Presently, there has been a tremendous advancement in the development of elec-
tric vehicles (EVs). EVs as part of demand-side management provide more bene-
fits and environmental advantages [1]. Several countries of the world have started
adopting EVs for de-carbonization and mobile energy storage to achieve a green
city [2]. As the number of EV continues to increase, there is a need to create a
charging infrastructure. Authors in [3] and [4] have proposed an optimal settings
of charging station (CS) and optimal scheduling to minimize vehicular resources
and time. However, authors do not give emphasis on privacy related issues of
EV such as location, price and consumption. Traditionally, EV is controlled and
monitored by a centralized system [5]. Besides, the centralized system also faces
issues of privacy and security like other known centralized schemes [6]. Also,
the centralized system lacks the ability to enforce the decision-making process
Springer Nature Switzerland AG 2020
L. Barolli et al. (Eds.): BWCCA 2019, LNNS 97, pp. 67–80, 2020.
68 O. Samuel et al.
on autonomous EVs. Solutions for aforementioned problem include peer-to-peer
and decentralization via blockchain [7]. The Table 1provides description of the
parameters or variables used throughout this paper.
Table 1. Parameters and variables
Notations Descriptions
min Minimum acceptance probability
kThe kth charging station’s (CS) assignment probability
nThe nth electric vehicle’s (EV) acceptance probability
bi,j and zj,i Row and column stochastic matrices
bThe bth blockchain offered price by CS
kThe kth CS’s selection probability based on Pr
band dk
nDistances of nth EV from the kth CS
gband prbThe broadcast parameters of distance and offered price, respectively
lap(y)Cumulative Laplace distribution for the given input y
Nand N+Cardinality of the out-bound and in-bound flow for ith nodes and jth vertices
nEnergy the nth EV required from CS
The concept of blockchain is introduced in 2008 by Satoshi Nakamoto [8]and
Bitcoin is its first application. Blockchain is a shared ledger that facilitates the
process of recording transaction and tracking assets in a distributed network.
Within the last decade, blockchain is now the focus of many researchers, stake-
holders and industries spanning from voting, healthcare, finance, real estate, util-
ities [9], Internet of Things [10,11], wireless sensor network [12,13]. Blockchain
provides decentralization, immutability, trustfulness [14], traceability, secure
environment and data storage. Advantages of blockchain include real-time trans-
action and payment; quick response time; avoids duplication; prevents fraud and
cyber attacks; minimizes time-consuming vetting process and provides trans-
Several studies in [1521] used blockchain as a privacy-preserving mechanism
for data aggregation; privacy protection and energy storage; secure classification
of multiple data; incentive announcement network for a smart vehicle; crowdsens-
ing applications; dynamic tariff decision, payment mechanism for vehicle-to-grid,
data right management [22], and incentive for lightweight clients [23]. However,
blockchain solution is inefficient to tackle data mining and linking attacks [24].
These attacks take advantage of exposed information stored in a block and pri-
vacy is disclosed by linking records of other datasets.
From the literature above and the inspiration obtained from the work of [25],
we derive our problem statement based on the following analogies: assuming
we have a setup of centralized server coordinating the trading between EVs
and CSs. The server publishes CSs with offered prices and locations and EVs
autonomously choose the preferred CSs. The benefit is that the EVs do not need
to disclose their exact locations and the server does not know the CSs which EVs
Electric Vehicles Privacy Preserving Using Blockchain in Smart Community 69
have selected. The disadvantage is that the server has no control over the assign-
ment of CSs and the EVs can select CSs based on their distances and offered
prices. In contrast to the centralized approach, we have a setup of blockchain-
based energy trading between EVs and CSs. The EVs send their locations and
the required quantity of energy to the blockchain. The blockchain controls and
allocates nearby CSs to the EVs while maximizes EVs’ acceptance rates. How-
ever, EVs’ private information such as locations are revealed to the blockchain
during the payment process, which raise privacy concerns to the owners of EV.
In a privacy-preserving perspective, information recorded on blockchain may
raise privacy concern [26]. Nevertheless, the traditional system cannot protect
EVs’ information within this scenario. Hence, we propose a system that protects
EVs’ location while ensuring fair energy trading. The proposed system will pre-
vent re-identification attack via private blockchain since EVs’ transaction records
are stored across different networks. Thus, honest-but-curious EVs cannot infer
the identity of EVs through observational studies.
The organization of the paper is as follows: Sect. 2provides the paper con-
tributions while Sect. 3discusses the proposed system model as well as problem
formulations. Simulation results are discussed in Sects. 4and 5provides the con-
clusion and future work.
2 Contributions
In this section, the contributions of this paper are as follows.
1. We protect EV’s privacy from future blockchain based data transmission by
defending EV against a possible breach. Our proposed scheme ensures com-
plete accuracy since it is implemented using real dataset and it is efficiently
adoptable since all computations are done off-chain, thereby reducing the
number of computing resources on the chain.
2. Differential privacy is proposed by using the consensus energy management
algorithm [27] to conceal the broadcast information.
3. Two types of blockchain are proposed: private blockchain located at rural
area achieves the following: prevents re-identification and data mining attacks
due to membership restrictions and provides subsidy for charging; and public
blockchain located in urban area resolves the scalability issue.
3 Proposed System Model and Problem Formulations
3.1 System Overview
In the proposed system in Fig. 1, three fundamental entities with distinct func-
tionalities are studied. Firstly, the EV as an entity that requires energy for
charging, secondly, CS as an entity that acts as an energy provider. However,
CS gets charged by the main grid if its internal generated energy is insufficient.
In addition, the CS charged EV on the basis of the offered price [1]. Lastly, the
70 O. Samuel et al.
aggregator (blockchain) acts as a broker between the EV and CS for fair energy
transactions. EVs send charging request and location to the aggregator; aggre-
gator broadcasts this information to the blockchain network. CSs who meet this
requirement response back with offered price and location to the aggregator.
Aggregator reports this information to the requesting EV and CS is assigned to
EV on the basis of price and location.
Fig. 1. Proposed system. EV: electric
vehicle, and CS: charging station.
Fig. 2. Illustration of the system net-
3.2 Blockchain Based Location Privacy Preserving with Differential
In energy trading, the EV’s charging request task is denoted as RDT, while CS’s
discharging response task given as RST. Thus, the rationality of RDT and RST
are as follows:
RDT: In the blockchain, EVs addresses are anonymous; hence, the blockchain
receives all RDT from EVs and broadcast them. However, blockchain is unaware
of the locations and charging request of EVs. In addition, EVs choose charg-
ing locations based on reduced Pr
band dk
n, to minimize traveling costs. Thus,
blockchain has no control over the activities of EVs [25].
RST: CSs send lkand Pr
bto the blockchain. Blockchain assigns CS to EV based
on dk
n. Thus, the blockchain controls activities of EVs. Since RDT and RST are
known to the blockchain, which may raise privacy concerns [25]. A blockchain
knowledge base (BKB) that stores all records of CSs and EVs, respectively is
where EVnand CSkare lists of EVs and CSs, respectively. Hnand Hkare the
histories of EVs and CSs; while, lnand lkare the locations of EVs and CSs,
Electric Vehicles Privacy Preserving Using Blockchain in Smart Community 71
3.2.1 Adversary Model
We assume that there are honest-but-curious aggregators on the blockchain net-
work. These curious aggregators disclose information of EVs for selfish interest
or financial benefits. Also, the curious aggregator known as CurAg can join
the public or private blockchain to gain information [25]. Moreover, the EV’s
current, past, and future location can be leaked by CurAg during charging and
payment process. The attacker can be any participant in the blockchain network.
Although, an attacker in the public blockchain can access transactional records
of EVs, while attacker as EV can join the private blockchain to get transaction
records of other EVs. Besides, access to other private blockchain is hindered
due to membership restrictions [25]. Attacker as an aggregator may have access
to transactional records of his own dataset. However, it is impossible to access
records of other aggregators [25].
3.2.2 Privacy-Preserving in Blockchain
The use of blockchain provides anonymization, ensures that EV fulfilled an
agreement with the CSs and decentralized the system to prevent a single point
of failure. Also, private blockchain prevents the re-identification attack since
each aggregator has distinct transactional history. Thus, it is infeasible for an
attacker to access transactional records of all aggregators without poisoning their
records [25].
Process of blockchain:
1. Registration: EVs and CSs are required to register with their private sk and
public pk key for verification and authentication.
2. CS price mechanism: the price offered to EV is determined by CS.
3. Smart contract: CSs and EVs are required to make an initial token deposit
which prevents double spending and false declaration of information.
4. EV’s assignment: EV prefers CS on the basis of lnand Pr
b, and make requests
accordingly. However, EV is validated based on uploaded lnin the urban area;
thereby, granting access to a specific CS.
5. CS’s selection: Blockchain ensures that CSs have the available discharging
capacities from the urban area to charge EVs. Otherwise, a new block is
created with deduction of the deposited token from CS’s account.
6. Consensus: EVs make charging request to the blockchain. Miner validates
the authenticity of the request. In this paper, proof of authority (PoA) is
used [28]. If requests are accepted, then payment transfer is made to CS’s
wallet account. Otherwise, if the claim is falsified, the token deposit is used
as a penalty.
Payment process: EVs wish to get charged at the closest possible distance
to their locations. Assuming all CSs sell energy at a fixed price, the acceptance
probability of EV will drop. Thus, the acceptance of EV is enhanced if CSs
discharge at different offered prices. Hence, acceptance probability of EV is cal-
culated in Eq. (2)[25].
72 O. Samuel et al.
n,k Ap
k=1(1 Ap
We assume CSs covers all lnof EVs, while some CSs do not cover EV’s ln.
This scenario is depicted in Fig. 1. Thus, the acceptance probability of EV is
proportional to the lkof CS. However, from Fig. 1, the CS enclosed in green
circle gets the highest acceptance by EVs since it covers all locations. The CS’s
assignment probability is calculated in Eq. (3); where R= 3 is the number of
regions. While the minimum distance of EV from CS is calculated in Eq. (4)[25].
min =2r, (4)
We consider the isolated CS, i.e., CS that covers only few EVs’ location; hence,
the average distance AV Gd
iso is calculated by counting Rwithin EV’s maximum
travel distance to CS as given in Eq. (5)[25]; where r= 2 is a constant value.
iso =dmax
n,k r. (5)
The CS’s selection probability is solved as the hyperbolic function of the Pr
and dk
nand given in Eq. (6)[25].
0, if otherwise, (6)
where αis a constant value.
Assumptions: from Eq. (6), CS with lower distance and minimum offered
price is selected with high probability; CS with higher distance and minimum
offered price is selected with low probability, whereas, CS whose distance is more
than the maximum distance of the concerned EV with higher offered price is not
To further protect EV’s location as well as the amount paid to CS, {, δ}-
differential privacy is proposed in this paper. The communication between EVs
and CSs formed a directed graph G, such that G={V,E}, where Vis a set of
nodes and Eis set of edges. V=NKand lets {j, i}∈Eif and only if node
icommunicates with node j[27]. Node iis the out-bound of node j; however,
self loop, i.e., {j, j}is not considered in this paper [27]. We derive the in-bound
and out-b ound values from Fig. 2as given in Table 2.
Electric Vehicles Privacy Preserving Using Blockchain in Smart Community 73
Table 2. Cardinality of in-bound and out-bound derived from Fig. 2.
A B C D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
N5 7 7 5 1 1 2 2 1 1 2 2 2 1 3 1 2 2 1 3 3 1 1 3
N+5 7 7 5 1 2 4 2 2 4 3 4 1 1 3 1 3 1 2 2 1 1 2 2
In Table 2, stochastic row and column matrices are generated using Eqs.(8)
and (9), respectively [27].
bi,j =
|N+|+1 ,ifiN+
i=1 bi,j ,ifi=j
zj,i =
|N|+1 ,ifiN+
i=1 zj,i,ifi=j
We generate the blockchain broadcast information about the dk
nand Pr
Eqs. (10) and (11), respectively [27].
n,k ,ifiN+
n,k ,ifiN
i=1 bi,j gb+ηprb,ifi=j,
where, dmin
n,k and dmax
n,k are minimum and maximum distances of EVs from CSs;
whereas, Prmin
band Prmax
bare minimum and maximum offered prices and
η=0.8 is scaling factor. The broadcast information is modified by adding a
cumulative Laplace noise as given in Eqs. (12) and (13). Thus, Eq. (1) is updated
with the new broadcast information as given in Eq. (16).
gb+1 =gb+1bi,j +lap(y),ifiN+
gbbi,j +lap(y),ifiN,(12)
prb+1 =zj,iprb+1 +lap(y),ifiN+
74 O. Samuel et al.
BKB(b+ 1) is broadcast to the blockchain network. Even if an attacker has the
broadcast information, it will be impossible to infer the ownership of information.
Thus, we define the privacy risk of EVs Rval
i,n over their private information
BKB(b+1) as[29]:
i,n (BKB(b+ 1)) = PC(BKB(b+ 1)).SL(BKB(b+ 1)),(17)
where the privacy concern PC(BKB(b+ 1)) ∈{0,1}and sensitivity level
SL(BKB(b+ 1)) ∈{0,1}. Using (, δ)-differential privacy, the SL(BKB(b+ 1))
is obtained by finding their differences (f(G1)f(G2)), i.e., the set G1and
G2differing on at most one element [29]. However, and δare privacy levels of
price and location with given values of 1, 2, 3, 4, 5 and 6, respectively.
3.3 Blockchain Smart Contract
Figure 3shows smart contract for the proposed scheme. Blockchain is unaware
of when and where EV will go; hence, EV’s exact location is preserved. Since
CS status in public blockchain differs from that of a private blockchain. Thus,
blockchain ensures CS is available in the urban context before assigning EV
to prevent void contract [25]. Similarly, private blockchain must verify if CS
is assigned to public blockchain or not before assigning EV to prevent void
contract. For EV to make a charge request, its credit value (CR) is verified and
authenticated with the sk and pk to ensure EV has been registered. If CR is
not empty, EV can make a charge request by uploading its region and Preq
to the aggregator. The aggregator verifies region via region identity Rid.The
Rid is used to determine if EV is in a rural area (private blockchain) or urban
area (public blockchain) for which the specified offered prices are determined.
Also, the offered prices for types of EV are verified via EV identity EV id. Once
CS supplied the required charging, payment is made to CS’s wallet account
by concerned EV. If the current time of CS is more than the agreed due time
CSdueTime to verify the payment, a token deduction is made against such CS.
Electric Vehicles Privacy Preserving Using Blockchain in Smart Community 75
Fig. 3. Smart contract.
4 Simulation Results
Simulation results and discussions are presented in this section.
4.1 Experimental Setup
We develop our blockchain using the ethereum platform [30] 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. Also, we customize 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. Simulation results are
generated using MATLAB2018. The hardware platform is a Dell i5, with 8 GB
ram and CPU of 1.60 Hz and 1.80 GHz.
4.2 Simulation Dataset
In this section, simulation results describe the evaluation of the proposed
blockchain based privacy preserving for EV’s location. In this paper, 20 EVs
and 4 CSs are used. The offered prices by the four CSs and the real distance
between EVs from CSs are taken from [1]. The EV’s battery capacity and CSs’
specifications are also taken from [1] (Figs. 4and 5).
76 O. Samuel et al.
Fig. 4. Price offered by four CSs [1]. Fig. 5. Distance of EVs from CSs [1].
4.3 Evaluation of EV’s Selection and CS’s Assignment Probability
This section discusses the EV’s acceptance and CS’s assignment probability. EV
accepts CS with the closest distance from its location. By assumption, if all CSs
announce the same offered for charging of EV, then EV’s selection probability
will be reduced. Using Eqs. (6) and (7), the Fig. 6shows the CS’s selection prob-
ability is close to the maximum limit. The results further show that the EV’s
acceptance of CS can only be achieved if the number of counted regions fall
within the EV’s maximum distance to the CS. Thus, the probabilities of all CSs
either as an edge or as isolated for being selected will be increased. However, the
offered price by CS also determines its acceptance by EV. The CS with the clos-
est distance and the lowest offered price has a high probability of being accepted.
Also, the CS with the longest distance and lowest offered price is accepted with
a low probability. Nevertheless, if the distance to CS is more than the maxi-
mum distance of EV, CS may be rejected even if it offers the lowest price. Using
Eq. (2), the probability of CS being assigned to EV is based on distance and is
proportional to regions where the distance is covered.
Fig. 6. Various probabilities of CSs and EVs.
Electric Vehicles Privacy Preserving Using Blockchain in Smart Community 77
4.4 Privacy Preserving Evaluations Using the Proposed Blockchain
and Differential Privacy
This section discusses the (, δ)-differential privacy-preserving for the proposed
blockchain scheme.
In Figs. 7and 8, the individual EV privacy is protected against set theory
attack [26]. The results further explained that as the privacy level increases, the
risk revealing decreases as well. The proposed scheme also prevents linking based
attack via (, δ)-differential privacy which hindered adversary activities [26]. The
private blockchain approach of the scheme prevents data mining attack since
transaction records of EVs are scattered across different private network which
is strengthen by membership restriction.
Fig. 7. Risk revealing versus privacy
level for the offered price.
Fig. 8. Risk revealing versus privacy
level for the distance.
4.5 Computational Blockchain Cost Analysis
Creating a new block in blockchain requires strict verification process from an
authorized node. In this paper, PoA adopted from our previous work [28] where
Pagerank rank mechanism is used to select the node as the authorized node
on the basis of its reputation score. Hence, the latency of confirmation time is
reduced since only authorized node is allowed to create a block and computes
the assignment and selection probability off-chain, thereby reducing the number
of computing resources needed on the chain. From Fig. 3, the time complexity of
the smart contract is less than O(n) [25]. Hence, the computational burden has
no influence on the blockchain.
5 Conclusion
This paper examines that transactional record on blockchain may raise privacy
concern such as disclosing private information like location and price. Three ways
locations of EV are disclosed such as current, previous and future are examined.
To preserve the location privacy of EVs, a private blockchain is incorporated
78 O. Samuel et al.
which prevent re-identification attack due to membership restrictions. Thus, the
transactional record histories of EVs cannot be inferred by the attacker since
records are spread across the network. To further preserve the records, differential
privacy is exploited to conceal the records against observational studies. The CS’s
assignment and EV’s selection probability are derived based on the offered price
and location of EVs. Simulation results demonstrate that privacy is achieved
through risk revealing metric. Also, the proposed approach prevents semantic
based attack since private blockchain is involved; data mining and linking based
attack since differential privacy is used.
In the future, the neighboring energy trading where dynamic pricing is an
issue for charging the EVs in a smart community will be explored. Furthermore,
we intend to consider the initial state as the possible privacy breach, such that
even if an attacker has the exact knowledge about the initial state of other EVs,
it will be difficult to breach their privacy.
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... Early work by Satoshi Nakamoto on the world first cryptocurrency, Bitcoin [13], addressed the concept of employing anonymous internet users to communicate with one another in order to establish a decentralised payments in electronic system. Encryption-enabled computing nodes or networks that collaborate and execute transactions with the help of cryptography are referred to as a block or a network. ...
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Purpose: In this paper we utilise electric vehicle-based cloud edge (EVCE) computing, it is possible to integrate vehicle contexts in a seamless manner. With the increasing use of electric vehicles (EVs) in V2X, this is likely to become a trend. When it comes to information and energy exchanges, a hybrid cloud/edge computing system with EVs as a potential resource infrastructure presents considerable security challenges. In order to find context-aware vehicular applications, the viewpoints of information and energy interactions are taken into consideration. The use of distributed consensus has resulted in the creation of blockchain-inspired energy and data coins, which use the frequency of data contributions and the amount of energy contributions to demonstrate the proof of work for each coin. When it comes to protecting vehicle interactions, the industry, innovation, and infrastructure sectors of Envision2030 are confronted with a number of different security alternatives. Design/Methodology/Approach: The EVCE computing for mobile cloud architecture to the electric vehicle acting as the edges across the network. The unutilised energy resources, and communication and computational resources of EVs are pooled together and used for other purposes. Mobile cloudlets for electric vehicles are created using VANETs and other interconnected services that gets connected together. When EVs are parked for extended periods of time in different locations, they form a cooperative network of services. Incorporating flexible connected EVs into traditional cloud infrastructures enables contact with remote service providers, local area networks (LANs), as well as other organisations, while operating in the cloud computing mode. Findings/Result: The primary purpose of a blockchain application is to maintain a record of all of the transactions that have been made by the various members of the network. After the submitted transactions have been confirmed and arranged, a block is formed, and the outcomes of the transactions are put on the blockchain as transaction results. Originality/Value: When it comes to transactions, HLF three-stage revolutionary design, dubbed execute-order-validate, is reliant on the preceding steps of the transaction to function properly. Because the actual throughput is close to 100%, a 100 TPS transmit rate is achievable and sustainable Paper Type: Experimental Research
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This thesis examines the privacy preserving energy management issue, taking into account both energy generation units and responsive demand in the smart grids. Firstly, because of the inherent stochastic behavior of the distributed energy resources, an optimal energy management problem is studied. Distributed energy resources are used in the decentralization of energy systems. Large penetration of distributed energy resources without the precise cybersecurity measures, such as privacy, monitoring and trustworthy communication may jeopardize the energy system and cause outages, and reliability problem for consumers. Therefore, a blockchain based decentralized energy system to accelerate electrification by improving service delivery while minimizing the cost of generation and addressing historical antipathy and cybersecurity risk is proposed. A case study of sub-Sahara Africa is considered. Also, a blockchain based energy trading system is proposed, which includes price negotiation and incentive mechanisms to address the imbalance of order. Besides, the Internet of energy makes it possible to integrate distributed energy resources and consumers. However, as the number of users involved in energy transactions increases, some factors are restricting conventional centralized energy trading. These factors include lack of trust, privacy, fixed energy pricing, and demurrage fees dispute. Therefore, additive homomorphic encryption and consortium blockchain are explored in this thesis to provide privacy and trust. Additionally, a dynamic energy pricing model is formulated based on the load demand response ratio of prosumers to address the fixed energy pricing problem. The proposed dynamic pricing model includes demurrage fees, which is a monetary penalty imposed on a prosumer if it failed to deliver energy within the agreed duration. Also, a new threat model is designed and analyzed. Secondly, mobile prosumers, such as electric vehicles offer a wide range of sophisticated services that contribute to the robustness and energy efficiency of the power grid. As the number of vehicles in the smart grid grows, it potentially exposes vehicle owners to a range of location related privacy threats. For example, when making payments, the location of vehicles is typically revealed during the charging process. Also, fixed pricing policy and lack of trust may restrict energy trading between vehicles and charging stations. Therefore, a private blockchain system is proposed to preserve the privacy of vehicle owners from linking based attack while a public blockchain system is established to enhance energy trading. Various parameters are used to formulate a demand based pricing policy for vehicles, such as time of demand, types of vehicles and locations. Using the demand based pricing policy, an optimal scheduling method is designed to maximize the vehicles both social welfare and utility. An improved consensus energy management algorithm is proposed to protect the privacy of vehicle owners by applying differential privacy. The proposed system is robust against temporal and spatial location based privacy related attacks. Thirdly, blockchain is an evolving decentralized data collection technology, which costeffectively exploits residential homes to collate large amounts of data. The problems of blockchain are the inability to withstand malicious nodes, which provide misleading information that destabilize the entire network, lack of privacy for individual node and shared data inaccuracy. Therefore, a secure system for energy users to share their multi-data using the consortium blockchain is proposed. In this system, a credibility based Byzantine fault tolerance algorithm is employed as the blockchain consensus mechanism to achieve the fault tolerance of the system. Also, a recurrent neural network is used by certain honest users with credibility to forecast the energy usage of other honest users. A recurrent neural network operates on the collated data without revealing the private information about honest users and its gradient parameters. Moreover, additive homomorphic encryption is used in the recurrent neural network to secure the collated data and the gradient parameters of the network. Also, a credibility management system is proposed to prevent malicious users from attacking the system and it consists of two layers: upper and lower. The upper layer manages global credibility that reflects the overall readiness of honest users to engage in multi-data sharing. The lower layer performs local credibility that reflects certain feedback of honest users on the accuracy of the forecast data. Lastly, combining blockchain mining and application intensive tasks increases the computational cost for resource constrained energy users. Besides, the anonymity and privacy problems of the users are not completely addressed in the existing literature. Therefore, this thesis proposes an improved sparse neural network to optimize computation offloading cost for resource constrained energy users. Furthermore, a blockchain system based on garlic routing, known as GarliChain, is proposed to solve the problems of anonymity and privacy for energy users during energy trading in the smart grid. Furthermore, a trust method is proposed to enhance the credibility of nodes in the GarliChain network. Simulations evaluate the theoretical results and prove the effectiveness of the proposed solutions. From the simulation results, the performance of the proposed model and the least-cost option varies with the relative energy generation cost of centralized, decentralized and blockchain based decentralized system infrastructure. Case studies of Burkina Faso, Cote d’Ivoire, Gambia, Liberia, Mali, and Senegal illustrate situations that are more suitable for blockchain based decentralized system. For other sub-Sahara Africa countries, the blockchain based decentralized system can cost-effectively service a large population and regions. Additionally, the proposed blockchain based levelized cost of energy reduces energy costs by approximately 95% for battery and 75% for the solar modules. The future blockchain based levelized cost of energy varies across sub-Sahara Africa on an average of about 0.049 USD/kWh as compared to 0.15 USD/kWh of an existing system in the literature. The proposed model achieves low transaction cost, the minimum execution time for block creation, the transactional data privacy of prosumers and dispute resolution of demurrage fees. Moreover, the proposed system reduces the average system overhead cost up to 66.67% as compared to 33.43% for an existing scheme. Additionally, the proposed blockchain proof of authority consensus average hash power is minimized up to 82.75% as compared to 60.34% for proof of stake and 56.89% for proof of work consensus mechanisms. Simulations are also performed to evaluate the efficacy of the proposed demand based pricing policy for mobile prosumers. From the simulation results, the proposed demand based pricing policy is efficient in terms of both low energy price and average cost, high utility and social welfare maximization as compared to existing schemes in the literature. It means that about 89.23% energy price reduction is achieved for the proposed demand based pricing policy as compared to 83.46% for multi-parameter pricing scheme, 73.86% for fixed pricing scheme and 53.07% for the time of use pricing scheme. The vehicles minimize their operating costs up to 81.46% for the proposed demand based pricing policy as compared to 80.48% for multi-parameter pricing scheme, 69.75% for fixed pricing scheme and 68.29% for the time of use pricing scheme. Also, the proposed system outperforms an existing work, known as blockchain based secure incentive scheme in terms of low energy prices and high utility. Furthermore, the proposed system achieves an average block transaction cost of 1.66 USD. Besides, after applying the differential privacy, the risk of privacy loss is minimum as compared to existing schemes. Furthermore, higher privacy protection of vehicles is attained with a lower information loss against multiple background knowledge of an attacker. To analyze the efficiency of the proposed system regarding multi-data sharing, an experimental assessment reveals that about 85% of honest users share their data with stringent privacy measures. The remaining 15% share their data without stringent privacy measures. Moreover, the proposed system operates at a low operating cost while the credibility management system is used to detect malicious users in the system. Security analysis shows that the proposed system is robust against 51% attack, transaction hacking attack, impersonation attack and the double spending attack. To evaluate the proposed system regarding energy management of resource constrained blockchain energy users, a Jaya optimization algorithm is used to accelerate the error convergence rate while reducing the number of connections between different layers of the neurons for the proposed improved sparse neural network. Furthermore, the security of the users is ensured using blockchain technology while security analysis shows that the system is robust against the Sybil attack. Moreover, the probability of a successful Sybil attack is zero as the number of attackers’ identities and computational capacities increases. Under different sizes of data to be uploaded, the proposed improved sparse neural network scheme has the least average computational cost and data transmission time as compared to deep reinforcement learning combined with genetic algorithm, and sparse evolutionary training and multi-layer perceptron schemes in the literature. Simulation results of the proposed GarliChain system show that the system remains stable as the number of path requests increases. Also, the proposed trust method is 50.56% efficient in detecting dishonest behavior of nodes in the network as compared to 49.20% of an existing fuzzy trust model. Under different sizes of the blocks, the computational cost of the forwarding nodes is minimum. Security analysis shows that the system is robust against both passive and active attacks. Malicious nodes are detected using the path selection model. Moreover, a comparative study of the proposed system with existing systems in the literature is provided.
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Underwater Wireless Sensor Networks (UWSNs) are promising and emerging frameworks having a wide range of applications. The underwater sensor deployment is beneficial; however, some factors limit the performance of the network, i.e., less reliability, high end to end delay and maximum energy dissipation. The provisioning of the aforementioned factors has become a challenging task for the research community. In UWSNs, battery consumption is inevitable and has a direct impact on the performance of the network. Most of the time energy dissipates due to the creation of void holes and imbalanced network deployment. In this work, two routing protocols are proposed to avoid the void hole and extra energy dissipation problems due to which lifespan of the network will increase. To show the efficacy of the proposed routing schemes, they are compared with the state of the art protocols. Simulation results show that the proposed schemes outperform their counterpart schemes. By keeping in mind the emerging security issues in sensor networks, we have proposed a blockchain based trust model for sensor networks to enrich the security of the network. Additionally, this model provides security along with data immutability. We have used a private blockchain because it has all the security features that are necessary for a private sensor network. Moreover, private blockchain cannot be accessed by using the Internet. In the proposed trust model, the Proof of Authority (PoA) consensus algorithm is used due to its low computational power requirement. In PoA consensus mechanism, a group of the validator is selected for adding and maintaining blocks. Moreover, smart contracts are used to validate and transfer cryptocurrency to service providers. In the end, transaction and execution costs are also calculated for each function to testify the network suitability.
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In a research community, data sharing is an essential step to gain maximum knowledge from the prior work. Existing data sharing platforms depend on trusted third party (TTP). Due to involvement of TTP, such systems lack trust, transparency, security and immutability. To over come these issues, this thesis proposed a blockchain based secure data sharing platform by leveraging the benefits of interplanetary file system (IPFS). A meta data is uploaded to IPFS server by owner and then divided into n secret shares. The proposed scheme achieves security and access control by executing the access roles written in smart contract by owner. Users are first authenticated through RSA signatures and then submit the requested amount as a price of digital content. After the successful delivery of data, a user is encouraged to register reviews about data by announcing customer incentives. In this way, maximum reviews are submitted against every file. In this scenario, decentralized storage, Ethereum blockchain, encryption and decryption schemes and incentive mechanism are combined. To implement the proposed scenario, smart contracts are written in solidity and deployed on local Ethereum test network. The proposed scheme achieves transparency, security, access control, authenticity of owner and quality of data. In simulation results, an analysis is performed on gas consumption and actual cost required in terms of USD, so that a good price estimate can be done while deploying the implemented scenario in real setup. Moreover, computational time for different encryption schemes are plotted to represent the performance of implemented scheme, which is shamir secret sharing (SSS). Results show that SSS shows least computational time as compared to advanced encryption standard (AES) 128 and 256.
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Decision fusion is used to fuse classification results and improve the classification accuracy in order to reduce the consumption of energy and bandwidth demand for data transmission. Decentralized classification fusion problem was the reason to use belief function based decision fusion approach in Wireless Sensor Networks (WSNs). With the consideration of improving the belief function fusion approach, we have proposed four classification techniques namely Enhanced K-Nearest Neighbor (EKNN), Enhanced Extreme Learning Machine (EELM), Enhanced Support Vector Machine (ESVM), and Enhanced Recurrent Extreme Learning Machine (ERELM). In addition, WSNs are fallible to errors and faults because of their different software, hardware failures, and their deployment in diverse fields. These challenges require efficient fault detection methods to be used to detect faults in WSNs in a timely manner. We induced four type of faults: offset fault, gain fault, stuck-at fault, and out of bounds fault and used enhanced classification methods to solve the sensor failure issues. Experimental results show that ERELM has given the first best result for the improvement of belief function fusion approach. The other three proposed techniques ESVM, EELM, and EKNN have provided the second, third, and fourth best results, respectively. Proposed enhanced classifiers are used for fault detection and are evaluated using three performance metrics ,i.e., Detection Accuracy (DA), True Positive Rate (TPR), and Error Rate (ER). In this thesis, the owner of the (Internet of Thing) IoT device can generate revenueby selling IoT device’s data to interested users. However, on the other hand, users do not trust the owner of IoT device for data trading and are not confident about the quality of data. Traditional data trading systems have many limitations, as they involve third party and lack: decentralization, security and reputation mechanisms. Therefore, in this thesis, we have leveraged the IoTs with blockchain technology to provide trustful data trading through automatic review system for monetizing IoT’s data. We have developed blockchain based review system for IoT data monetization using Ethereum smart contracts. Review system encourages the owner to provide authenticated data and solve the issues regarding data integrity, fake reviews and conflict between entities. Data quality is ensured to users through reviews and ratings about the data, stored in blockchain. To maintain the data integrity, we have used Advanced Encryption Standard (AES)-256 encryption technique to encrypt data. All transactions are secure and payments are automated without any human intervention. Arbitrator entity is responsible to resolve problems between data owner and users. Incentive is provided to users and arbitrator in order to maintain the user participation and honesty. Additionally, Ethereum blockchain system requires gas for every transaction. Simulations are performed for the validation of our system. We have examined our model using three parameters: gas consumption, mining time and encryption time. Simulations show that the proposed methods outperform the existing techniques and give better results for belief function and fault detection in datascience WSNs. Additionally, blockchain based data trading in IoT system requires gas for every transaction. We have examined our model using three parameters: gas consumption, mining time and encryption time.
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Wireless Sensor Networks (WSNs) are vulnerable to faults because of their deployment in unpredictable and hazardous environments. This makes WSN prone to failure such as software, hardware, and communication failures. Due to the sensor’s limited resources and diverse deployment fields, fault detection in WSNs has become a daunting task. To solve this problem, Support Vector Machine (SVM), Probabilistic Neural Network (PNN), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Random Forest (RF), and Convolutional Neural Network (CNN) classifiers are used for classification of gain, offset, spike, data loss, out of bounds, and stuck-at faults at the sensor level. Out of six faults, two of them are induced in the datasets, i.e., spike and data loss faults. Likewise, sensors embedded mobile phones are used for the collection of data for some specific task which can effectively save cost and time in Crowd Sensing Network (CSN). The quality of collected data depends on the participation level from all entities of CSN, i.e., service provider, service consumers and data collectors. In comparison with the centralized traditional incentive and reputation mechanisms, we propose a blockchain based incentive and reputation mechanism for CSNs, which mainly consists of three smart contracts. The incentives are used to stimulate the involvement of data collectors and motivate the participants to join the network. Also, the issue of privacy leakage is tackled by using Advanced Encryption Standard (AES128) technique. In addition to that, a reputation system is implemented to tackle the issues like untrustworthiness, fake reviews, and conflicts among entities. Through registering reviews, the system encourages data utilization by providing correct, consistent and reliable data. Furthermore, the results of first scenario are compared on the basis of their Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC), and F1-score. In this thesis, a comparative analysis is performed among the classifiers mentioned previously on real-world datasets and simulations demonstrate that the RF algorithm secures a better rate of fault detection than the rest of the classifiers. Similarly, the second scenario is evaluated through analyzing the gas consumption of all the smart contracts, whereas, the encryption technique is validated through comparing the execution time with base paper technique. Lastly, the reputation system is inspected through analyzing the gas consumption and mining time of input string length.
Conference Paper
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Internet of Things (IoTs) is widely growing domain of the modern era. With the advancement in technologies, the use of IoTs devices also increases. However, security risks regarding service provisioning and data sharing also increases. There are many existing security approaches, although these approaches are not suitable for IoT devices due to their limited storage and limited computation resources. These secure approaches also require a specific hardware. With the invention of blockchain technologies, many security risks are eliminated. With the help of blockchain, data sharing mechanism is also possible. In this paper, we proposed a novel secure service providing mechanism for IoTs by using blockchain. We introduced cloud nodes for maintaining the validity states of edge service providers. The rating and cryptocurrency is given to edge servers. Given rating and incentive is stored in cloud node and updated with respect to time. The smart contract is proposed to check the validity state of the edge server as well as compare and verify the service provided by edge servers. In our proposed system we perform service authentication at cloud layer as well as edge server layer. Moreover, by using Proof of Authority (PoA) consensus mechanism overall performance of our proposed system also enhanced.By experimental analysis it is shown, our proposed model is suitable for resource constrained devices.
Conference Paper
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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