Electric Vehicles Privacy Preserving using Blockchain in Smart Community

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 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.

Full text

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
omajiman1@gmail.com, nadeemjavaidqau@gmail.com
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
c
Springer Nature Switzerland AG 2020
L. Barolli et al. (Eds.): BWCCA 2019, LNNS 97, pp. 67–80, 2020.
https://doi.org/10.1007/978-3-030-33506-9_7

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
Ap
min Minimum acceptance probability
Ap
kThe kth charging station’s (CS) assignment probability
Ap
nThe nth electric vehicle’s (EV) acceptance probability
bi,j and zj,i Row and column stochastic matrices
Pr
bThe bth blockchain offered price by CS
CSsel
kThe kth CS’s selection probability based on Pr
band dk
n
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
N−and N+Cardinality of the out-bound and in-bound flow for ith nodes and jth vertices
Preq
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-
parency.
Several studies in [15–21] 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-
work.
3.2 Blockchain Based Location Privacy Preserving with Differential
Privacy
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
proposed.
BKB ={EVn,CS
k,d
k
n,A
p
k,CSsel
k,l
n,l
k,H
n{ln,Preq
n},
Hk{lk,Pr
b}},(1)
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,
respectively.

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.
Ap
n=dmax
n,k −Ap
min
dmax
n,k
;0≤Ap
k≤1,(2)
Ap
k=1−(1 −Ap
n)R.(3)
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].
Ap
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.
AV Gd
iso =dmax
n,k −r. (5)
The CS’s selection probability is solved as the hyperbolic function of the Pr
b
and dk
nand given in Eq. (6)[25].
CSsel
k=ex−ex
ex+e−x,ifd
k
n≤dmax
n,k
0, if otherwise, (6)
where,
x=αPr
b
dk
n
;0<CS
sel
k≤1,(7)
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
selected.
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=N∪Kand 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
N−5 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 =⎧
⎪
⎨
⎪
⎩
1
|N+|+1 ,ifi∈N+
1−|N+|
i=1 bi,j ,ifi=j
1
|N+|,ifi=j,
(8)
zj,i =⎧
⎪
⎨
⎪
⎩
1
|N−|+1 ,ifi∈N+
1−|N−|
i=1 zj,i,ifi=j
1
|N−|,ifi=j.
(9)
We generate the blockchain broadcast information about the dk
nand Pr
busing
Eqs. (10) and (11), respectively [27].
gb=⎧
⎪
⎨
⎪
⎩
dmin
n,k ,ifi∈N+
dmax
n,k ,ifi∈N−
|N−|
i=1 bi,j gb+ηprb,ifi=j,
(10)
prb=Prmin
b,ifi∈N+
Prmax
b,ifi∈N+,(11)
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),ifi∈N+
gbbi,j +lap(y),ifi∈N−,(12)
prb+1 =zj,iprb+1 +lap(y),ifi∈N+
zj,iprb+lap(y),ifi∈N−,(13)
where

74 O. Samuel et al.
lap(y)=σ
√2e2y,ify<0.5
−σ
√2e2(1−y),ify≥0.5,(14)
where
σ=max(y)−min(y)
,(15)
BKB(b+1)={EVn,CS
k,g
b+1,A
p
k,CSsel
k,l
n,l
k,
Hn{ln,Preq
n},H
k{lk,pr
b+1}}.(16)
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]:
Rval
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
n
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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