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Secure Data Sharing and Customized Services for Intelligent Transportation Based on a Consortium Blockchain

March 2020
IEEE Access PP(99):1-1

March 2020
PP(99):1-1

DOI:10.1109/ACCESS.2020.2981945

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In view of the security risks and centralized structure of traditional intelligent transportation system, we propose a novel scheme of secure data sharing and customized services based on the consortium blockchain (DSCSCB). The ciphertext-policy attribute-based proxy re-encryption algorithm has the function of keyword searching by dividing the key into an attribute key and a search key, which not only solves the problem hat proxy re-encryption algorithm cannot retrieve data, but also realizes data sharing and data forwarding. Moreover, the algorithm effectively controls the access permission of data, and provides a secure communication environment for the vehicular ad-hoc network (VANET). Service sectors, such as insurance companies, the traffic police and maintenance suppliers, obtain the corresponding ciphertext and then apply the mart contract to provide customized services for the onboard unit after decryption. Security analysis and performance evaluation demonstrate that our scheme not only meets the requirements of data sharing in the security and confidentiality, but also has obvious advantages in the overhead of computing and communication.

The structure of the onboard unit

…

Comparison of the computational overhead

…

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Digital Object Identifier 10.1109/ACCESS.2017.Doi Number

Secure Data Sharing and Customized Services for

Intelligent Transportation Based on a Consortium

Blockchain

DI WANG AND XIAOHONG ZHANG

School of Information Engineering Jiangxi University of Science and Technology, Ganzhou 341000, China

Corresponding author: Xiaohong Zhang (e-mail: xiaohongzh@263.net).

This work is jointly supported by the National Natural Science Foundation of China (Nos. 51665019, 61763017), Scientific Research Plan

Projects of Jiangxi Education Department (No. GJJ150621), Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202053,

20161BAB206145), and the Innovation Fund for Graduate Students in Jiangxi Province (Grant No: YC2017-S302).

ABSTRACT In view of the security risks and centralized structure of traditional intelligent transportation

system, we propose a novel scheme of secure data sharing and customized services based on the consortium

blockchain (DSCSCB). The ciphertext-policy attribute-based proxy re-encryption algorithm has the function of

keyword searching by dividing the key into an attribute key and a search key, which not only solves the problem

that proxy re-encryption algorithm cannot retrieve data, but also realizes data sharing and data forwarding.

Moreover, the algorithm effectively controls the access permission of data, and provides a secure

communication environment for the vehicular ad-hoc network (VANET). Service sectors, such as insurance

companies, the traffic police and maintenance suppliers, obtain the corresponding ciphertext and then apply the

smart contract to provide customized services for the onboard unit after decryption. Security analysis and

performance evaluation demonstrate that our scheme not only meets the requirements of data sharing in the

security and confidentiality, but also has obvious advantages in the overhead of computing and communication.

INDEX TERMS Consortium blockchain, data sharing, customized services, smart contract, intelligent

transportation.

Ⅰ. INTRODUCTION

With the improvement of living standards, vehicles have

become an indispensable tool of transportation in our daily

life. Intelligent transportation system [1], [2] combines

various technologies such as sensors, wireless communication,

and computer technology to establish a safe and efficient

transportation network, and thus provides comfortable and

convenient services for car owners. In recent years, with the

development of Internet of Things (IoT) and Mobile Internet

of Things (MIoT), the vehicular ad-hoc network (VANET)

has become an important part of the intelligent transportation

system, which has attracted extensive attention from

numerous scholars and researchers [3].

The onboard unit in the VANET could detect and

communicate with other onboard units, which means the

onboard unit can receive and transmit information, so that

other vehicles and management departments could obtain

accurate real-time traffic data. In order to detect the integrity

of data on two-way traffic roads, Aslam et al. [4] proposed a

two-direction and time-based data verification scheme

achieving safe transmission of traffic data. Although the

scheme does not rely on complex and expensive public key

infrastructure, it cannot resist man-in-the-middle attacks and

collusion attacks. Therefore, Feng et al [5] proposed a data

sharing scheme based on the cloud platform that can resist

man-in-the-middle attack and collusion attacks. In addition, to

prevent denial of service attacks, Hash problem based trust

cluster cooperative authentication scheme [6] was proposed,

which also reduced the overhead of pseudonym authentication.

Recently, a data aggregation scheme based on fully

homomorphic encryption [7] was used to protect the privacy

of identity and location in information interaction. Compared

with Paillier homomorphic encryption, the reference [7]

improved the security and reduced computational burden, but

it is inefficient. In general, the above schemes realize the

sharing of traffic data, but there are still some challenges:

1) Threats to security and privacy: It is easy for an attacker

to eavesdrop, tamper with, or forge data sent by onboard

units in an open wireless communication environment.

Worse still, the leakage of privacy data will threaten the

security and confidentiality of data. Once key

information is tampered with, it will not only reduce

traffic efficiency, but also threaten life safety.

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2) Centralization: The traditional intelligent transportation

system have a centralized structure, relying on a trusted

third party. Once the central node is attacked, the

security of the VANET will be threatened. In addition,

the maintenance and construction of the central node

requires plenty of financial and material resources.

Therefore, it is urgent to design a secure data sharing

scheme. Attribute-based encryption is an emerging

technology to achieve data access control and secure data

sharing [8]. Extended file hierarchy access control scheme

with ciphertext-polity attribute-based encryption [9] was

proposed for big companies with different hierarchical

departments, which implemented encryption for multiple files

on the same access level. In order to reduce the storage space

occupied by the ciphertext and computational overhead, the

attribute-based encryption algorithm with keyword search,

outsourcing decryption, and outsourcing key distribution [10]

was proposed, but it cannot achieve user revocation and

attribute revocation. Therefore, Li et al. [11] proposed the

ciphertext-policy attribute-based encryption with efficient

user revocation by introducing the concept of user groups.

Later, the issue that the attribute revocation was solved by

exploiting the concept of attribute groups [12]. To prevent the

insecure problems caused by the random oracle model, Ge et

al. [13] proposed a key-policy attribute-based proxy

re-encryption without random oracles, which improved

security via improving the re-encryption key query and

re-encryption query. Later, an attribute-based proxy

re-encryption and its adaptive security model [14] were

proposed and proved against chosen-ciphertext attack secure

in the adaptive model without random oracles. A novel

revocable identity-based broadcast proxy re-encryption [15]

was not only semantically secure in the random model, but

also allowed the proxy to revoke a set of delegates from the

re-encryption key.

To provide a secure and trusted communication

environment for intelligent transportation systems, Yang et al.

[16] proposed a lightweight anonymous authentication

scheme that can track malicious vehicles sending false

information and revoke their identities. Additionally, a

privacy protection scheme [17] used source authentication to

prevent the attackers from impersonating the legitimate node,

which utilized the cloud server to verify part of the ciphertext

without decrypting and filtered out the invalid traffic

information. In order to reduce the communication overhead

and the possibility of roadside unit collusion, Ni et al [18]

used the Bloom filter to provide drivers with privacy

protection parking navigation services, including identity

authentication, request authentication and driving guidance.

Kumar et al. [19] used elliptic curve encryption algorithm and

end-to-end authentication to protect the confidentiality of road

information, and took advantage of sandboxing method to

improve the security of data. Although the performance of

this scheme is better than other schemes, the computing cost

increases linearly with the size of data. The Secure

Signcryption Authentication Protocol [20] was used for

authentication, which can resist impersonation attacks, sybil

attacks, man-in-the-middle attacks and other network attacks.

In order to solve the problem that certificate revocation lists

need to occupy a large amount of network resources, the

semi-trust authentication scheme [21] combined key

distribution and certificateless signature, which not only

improved the efficiency of message verification, but also

reduced a lot of storage space. The above schemes improve

the confidentiality and security of data to some extent, but the

centralized structure in intelligent transportation still exists.

In recent years, there has been a global upsurge in

blockchain research, which has attracted numerous scholars to

combine blockchain with the VANET [22]. Subsequently,

Kang et al. [23] used blockchain technology to achieve secure

data sharing in the vehicular edge network, and applied a

three-weight subjective logic model to improve the quality of

shared data. Furthermore, in order to ensure the security of

access data, the blockchain-based distributed architecture [24]

applied a variable public key to protect the privacy of the

onboard unit and prevent location tracking. Yang et al. [25]

proposed the Proof-of-even consensus to verify the validity of

traffic events and fed back the correctness of traffic events.

Cebe et al. [26] proposed a lightweight license blockchain for

traffic accident forensics and forensic analysis, which is

convenient for solving traffic disputes efficiently and quickly.

Cheng et al. [27] proposed a semi-centralized traffic signal

regulation mode based on blockchain, which regulates traffic

signal lights according to the dynamic properties of vehicles,

which is not affected by the environment and equipment

installation, and has better effects in the environment with

sparse traffic flow. Jin et al. [28] proposed a charging

mechanism for electric taxis based on the consortium

blockchain, which improved the flexibility of charging

services and effectively solved the monopoly problem of

charging operators' charging information.

Different from the existing schemes, we propose a secure

data sharing and customized services based on the consortium

blockchain (DSCSCB). The onboard unit sends the ciphertext

to the roadside unit, and the verification node verifies the

ciphertext by the Ripple consensus. After the verification is

successful, the ciphertext is packaged to generate the block,

and then the block is connected to the blockchain. The

onboard unit sends a search service request to the smart

contract, the corresponding ciphertext is sent to the service

sector according to the keyword searched by the onboard unit.

The service sector decrypts and obtains the plaintext to

provide customized services for the onboard unit. If the

service department sends a search service request to the smart

contract, the corresponding ciphertext is converted to

re-encrypted ciphertext, which is sent to the relevant service

departments according to the keywords searched by the

service sector. The service sectors cooperate with each other

to provide more efficient and convenient services for the

onboard unit. In summary, the main contributions of this

paper are as follows:

1) We use the decentralization of the consortium blockchain

to break the data centralized management of traditional

intelligent transportation, prevent single point collapse

and data monopoly, and realize the data sharing without

the third-party intermediary. Service sectors apply smart

contracts to provide multi-dimensional and customized

services for onboard units, not limited to the

single-dimensional service.

2) Attribute-based proxy re-encryption algorithm is

proposed to implement keyword retrieval and proxy

re-encryption. According to keywords and attribute sets,

data access permissions are controlled, which prevents

collusion attacks and achieves secure and trusted data

sharing.

3) Security analysis and performance evaluation

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demonstrate that DSCSCB not only meets the security

requirements of data sharing, but also has more

advantages than other schemes in term of computational

overhead and communication overhead. Therefore,

DSCSCB is suitable for secure data sharing and

customized services for intelligent transportation.

The structure of this paper is organized as follows. Section

II introduces the technical preliminaries, mainly including

blockchain, bilinear mapping and encryption algorithm.

Section III details the proposed system framework. Sections

IV and V describe secure data sharing algorithm and

customized services respectively. Section VI analyzes the

security of proposed scheme and evaluates its performance.

Finally, we conclude this paper in Section VII.

Ⅱ. PRELIMINARIES

A. BLOCKCHAIN

The blockchain is a distributed ledger that originated from

Bitcoin [29] proposed by Nakamoto in 2008. With the rise of

digital currencies such as Bitcoin, blockchain has become a

new technology that is decentralized, secure, credible,

non-tamperable and traceable. Consensus mechanism is used

to generate the block. Asymmetric encryption and chain

structure can prevent data in blocks from being tampered with.

What’s more, smart contracts change traditional contract

formulation and fulfillment methods.

According to the nodes participating in the consensus,

blockchain is divided into the private blockchain, public

blockchain and consortium blockchain. Only a few nodes in

the private blockchain have write permissions and read

permissions, and the speed of reaching a consensus is fast, but

it is difficult for private blockchain to realize data sharing.

The public blockchain is completely decentralized, allowing

all nodes in the network to participate in the consensus, so the

demerit is that it takes an awfully long time to verify and

update data. However, the pre-selected nodes in the

consortium blockchain verify and record data, which speeds

up the generation of blocks and the nodes reach a consensus

faster.

Recently, blockchain has attracted great attention from the

government, investment companies and scientific research

institutions. It has been widely used in medical, Internet of

Things and other fields, as shown in Figure 1. Xu et al. [30]

proposed a healthchain based on the patient medical data to

control and management of electronic health records, avoid

leakage of sensitive data, and enhance privacy protection of

user. Zhang et al. used priority and cryptocurrency to

encourage electric vehicle users to use renewable energy,

which solved the problem of mismatch between supply and

demand of renewable resources [31]. Lei et al [32] proposed

an efficient and practical dynamic key management scheme,

which simplifies the key transmission process, and makes key

management easy to deploy and expand. In addition, some

scholars have applied blockchain to intelligent transportation.

Zhang et al [33] proposed a secure data sharing system for the

Internet of vehicles based on the blockchain, which employs

the fragmentation technology to improve the scalability of the

network and generate auxiliary blockchain to manage the data

of different entities. Adaptive traffic signal control mechanism

based on the consortium blockchain [34], which dynamically

regulates the period of traffic signal according to the road

information sent by vehicles, reduces waiting time and

alleviates traffic congestion. Therefore, the emergence of

blockchain brings new opportunities for intelligent

transportation.

Blockchain

Intelligent

transportation

Finance

Medical

Smart grid

Internet of

Things

FIGURE 1. Application scenarios of blockchain

B. BILINEAR PAIRING

and

are multiplicative cyclic groups with prime

orders

is the generator of

. Bilinear pairing [35]

1 1 2

:e G G G→

satisfies the following properties.

⚫ Bilinearity: for

,P Q G

and

r s Z

( , ) ( , )

r s rs

e P Q e P Q=

⚫ Computability: for

,P Q G

bilinear pairing

( , )e P Q

can be effectively calculated.

⚫ Non-degeneracy:

( , ) 1e P Q 

for

,P Q G

C. LINEAR INTEGER SECRET SHARING

Suppose





represents the single element

matrix

 

and

aZ

denotes the elements of the first

column of the multi-element matrix





( )

−



represents all the columns in

except the first column.

The linear integer secret sharing (LISS) [36] matrix

constructed with the access policy has the following

properties.

⚫ Each attribute

of the access policy

is expressed by

⚫ Suppose

and

represent matrices

1kv





and

2kv





, respectively. Any logic OR operation is

N N N=

, then

can be denoted as

( ) ( )

1 2 1 2 1k k v v

+  + −



, where the first column of

is the

result of cascading

and

, the next

( )

11v−

columns of

is the result of cascading the vectors in

with

zeros. The last

( )

21v−

columns of

the result of cascading

zeros and vectors in

can be expressed as

MaF



=



(1)

⚫ Suppose

and

represent matrices

3kv





and

4kv





, respectively. Any logic AND operation is

'34

N N N=

, then

can be denoted as

( ) ( )

3 4 3 4

k k v v

and

+  +



, where the first column of

and

is the

result of cascading

and

zeros, the second

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Access

column of

and

is the result of cascading

and

the next

( )

31v−

columns of

and

is the result of

cascading the vectors of

with

zeros. The last

( )

41v−

columns of

and

is the result of cascading

zeros with vectors in

and

can be expressed as

3 3 3

and

a a F

MaF



=



(2)

D. ATTRIBUTE-BASED PROXY RE-ENCRYPTION

The concept of proxy re-encryption was first proposed by

Blaze et al. [37] in 1998 and was not formally defined until

2006. Proxy re-encryption means that the proxy converts the

ciphertext into a ciphertext that the visitor can decrypt, while

the proxy cannot obtain any plaintext. The attribute-based

proxy re-encryption algorithm [38] only allows users who

satisfy the access structure to decrypt data, mainly including

the following seven algorithms:

⚫

Setup

is the system initialization algorithm. It takes as

input a security parameter



and the attribute set

. It

outputs the system parameters

Sparams

, a master key

MSK

and the system public key

SPK

. Expose

Sparams

and

SPK

public, but keep

MSK

secret. The

system initialization algorithm can be expressed as

( , ) ( , , )Setup X Sparams MSK SPK



→

⚫

KeyGen

is the key generation algorithm. Given the

system parameters

Sparams

, the master key

MSK

, the

system public key

SPK

, and the attribute set

SX

the user Cindy, the private key

and public key

of Cindy are generated. The key generation algorithm can

be described as

( , , , ) ( , )

C C C

KeyGen Sparams MSK SPK S SK PK→

⚫

Enc

is the data encryption algorithm. The ciphertext

is generated by the system parameters

Sparams

Cindy's public key

, the access structure

( , )M



and plaintext

. The encryption algorithm can be

expressed as

( , ,( , ), )

Enc Sparams PK M m C



→

⚫

ReKeyGen

is the re-encryption key generation algorithm.

Suppose Cindy is the data owner and Nancy is the data

visitor. Input the system parameters

Sparams

, a new

access structure

( , )M



, Cindy's private key

and

her attribute set

, and Nancy's public key

Output the re-encryption key

RK →

, that is,

Re ( ,( , ), , , )

C C N C N

KeyGen Sparams M SK S PK RK



→

⚫

ReEnc

is a re-encryption algorithm. The re-encrypted

ciphertext

is generated by the system parameters

Sparams

, the system public key

SPK

, the re-encryption

key

RK →

, and the ciphertext

. The re-encryption

algorithm can be represented by

Re ( , , , )

Enc Sparams SPK RK C C

→→

⚫

Dec

is the ciphertext decryption algorithm. Taking as

input the system public key

SPK

, Cindy's private key

, and the ciphertext

, the algorithm outputs the

plaintext

, that is,

( , , )

Dec SPK SK C m→

⚫

ReDec

is a re-encrypted ciphertext decryption algorithm.

Nancy's private key

is used to decrypt the

re-encrypted ciphertext

to get the plaintext

, that is,

Re ( , )

Dec SK C m→

Ⅲ. PROPOSED SYSTEM FRAMEWORK

OBU RSU

Master node

Block

Consortium blockchain

Insurance

company

Maintenance

Traffic

police

Service sector

MSSC

VPSC

CPSC

Smart contract

Trusted authority

Consensus

Block header

Previous block hash Timestamp

Transaction root

State rootReceipt root

Transaction 1 Transaction 2

Transaction 3 Transaction n

Mileage Speed Acceleration

Reputation value Fault report

Block body

Version



RMSC

SSC

FIGURE 2. DSCSCB system framework

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DSCSCB can not only realize secure data sharing but also

provide customized services for onboard units, the system

framework of which is shown in Figure 2. It mainly includes

the onboard unit, roadside unit, consortium blockchain,

trusted authority, consensus mechanism, smart contract and

service sector. The detailed definition of each entity is as

follows.

A. ONBOARD UNIT

The structure of the onboard unit is shown in Figure 3. The

onboard unit (

OBU

) is equipped with a communication

module, a sensor, a memory unit, an embedded computer, etc.

Among them, the sensor is used to collect driving data of the

vehicle, such as speed, mileage, working status of automobile

parts, etc., and sends them to the

OBU

. The

OBU

integrates driving data to form plaintext, then the plaintext

and the access structure are encrypted to generate the

ciphertext, which is sent to the

RSU

by dedicated

short-range communication [39]. Once an

OBU

is produced,

a unique identity is assigned to it. The

OBU

as the data

owner is responsible for encrypting the plaintext and

preseting the access structure of the data. The service sector

can access data only if the access policy is met.

Sensor

Memory

Embedded

computer

Comm unication

module

FIGURE 3. The structure of the onboard unit

B. ROADSIDE UNIT

Compared with the

OBU

, the

RSU

has stronger

computing power and larger storage. The

RSU

is generally

installed on both sides of the road every kilometer or even

shorter distance to ensure a high quality communication

environment even in a traffic congestion. The

RSU

communicates with the

OBU

through a wireless network,

whereas it communicates with other

RSUs

through a wire

network. The

RSU

is required to be registered in the

blockchain with identity when it is used. The

RSU

with

better performance is pre-selected as an accounting node to

verify traffic data sent by the

OBU

C. TRUSTED AUTHORITY

It is assumed that the trusted authority (

) has strong

computing power and huge storage space in the whole

network, which is secure and hard to be captured.

mainly responsible for generating the system parameters, the

master key, private keys, search keys and re-encryption keys.

D. CONSORTIUM BLOCKCHAIN

For more secure data sharing and less network overhead, it

is appropriate to adopt the consortium blockchain. The block

body of the consortium blockchain mainly records the

location, speed, reputation value and other data. The receipt

root in the block head stores customized services provided by

the service sector. For example, the insurance contract

specially formulated by the insurance company according to

the driving style of the owner. The transaction root mainly

records the driving data of the

OBU

, such as acceleration,

mileage, etc. As for the status root, it saves the overall status

of the service sector. For instance, data accessed by the

service sector.

E. CONSENSUS MECHANISM

In this paper, Ripple Consensus is used to verify the data

and each

RSU

is a verification node. Nodes with better

performance (more computing power and better hardware and

software environment) are selected from the verification

nodes to join the master node list. The

RSU

stores the data

sent by the

OBU

to the local buffer pool, and then the local

data is aggregated and sent to the master node for verifying.

Master nodes verify the data and send the result to the

RSU

The data confirmed by more than 80 percent of the master

nodes is packaged into blocks, which are then connected to

the consortium blockchain.

F. SMART CONTRACT

The smart contract was first proposed by cryptographer

Nick Szabo in 1994 [40], which means implementing contract

terms by using computerized transaction protocols and user

interfaces. Blockchain periodically traverses the trigger

condition and the state of the smart contract. Once the trigger

condition is met, the smart contract is invoked to control and

manage the nodes in the blockchain. DSCSCB contains

automatic claim and insurance pricing smart contract (CPSC),

traffic violation penalty smart contract (VPSC), maintenance

service smart contract (MSSC) and search service smart

contract (SSC).

G. SERVICE SECTOR

(1) THE INSURANCE COMPANY

Whenever a traffic accident happens, the car owner makes

a claim on the insurance company and then pays the credit

value to the CPSC address as a collateral to guarantee the

solvency of the owner and avoid false requests. The insurance

company obtains mileage, acceleration, vehicle speed, vehicle

device status (such as brake pads, steering wheel control,

engine, throttle control) and other related data from the

blockchain according to the access policy. CPSC is utilized to

traffic accident arbitration, evaluate insurance premiums,

automatic claims and financial settlement. After the claim is

completed, CPSC pays the insurance company the credit

value as the service fee.

The insurance company has already established a database

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based on relevant data provided by the

OBU

and has made

use of data analysis with CPSC to formulate customized

insurance contracts for car owners with different driving style,

thereby reducing the insurance cost. After the insurance

contract has been made, the credit value will be paid as a

reward to the onboard unit that provides data.

(2) THE TRAFFIC POLICE

The traffic police obtains information such as the speed,

position, and lane change of the vehicle and so on from the

blockchain according to the access policy, and determines

whether the driver complies with the traffic rules. Once the

driver has violated the rules, the traffic police uses VPSC to

deduct the

OBU

's credit value and impose a fine. VPSC

enhances drivers' awareness of complying with traffic rules,

so it can effectively improves traffic safety.

(3) THE VEHICLE MAINTENANCE

OBU

submits a service request to the vehicle maintenance

in case of

OBU

failure. The credit value is paid to MSSC

address as a mortgage to ensure that the owner has the ability

to pay the service fee and avoid false requests. After receiving

the request, the vehicle maintenance obtains the working

status data of vehicle components from the blockchain

according to the access policy. The operation state of parts

and equipment is analyzed and the vehicle maintenance

system model is established to determine the cause of the

OBU

failure. Then, the vehicle maintenance uses the MSSC

to develop a maintenance plan for the faulty

OBU

. After the

OBU

repair is completed, the MSSC pays the credit value as

a service fee to the vehicle maintenance.

The vehicle maintenance obtains the relevant data of the

OBU

according to the access structure. Data analysis and the

MSSC are used to formulate different vehicle maintenance

plans. After the maintenance plans have been made, the credit

value will be paid as a reward to the

OBU

that provides

data.

Ⅳ. SECURE DATA SHARING

Take vehicle maintenance service as an example, as shown

in Figure 4. Assume that

OBU

has failed, we want to carry

out fault diagnosis and maintenance in the vehicle

maintenance company within 3km of home.

OBU

encrypts

relevant data such as the working status of vehicle

components under

 

1 3 L within km away from home=

condition to generate the ciphertext and send the ciphertext to

the

RSU

. The

RSU

node records the successfully verified

ciphertext into the block, and then connects the block to the

blockchain. The SSC searches for a vehicle maintenance

that satisfies

 

1 3 L within km away from home=

and sends

the ciphertext to it, so that

can provide customized

services for the

OBU

. If the maintenance level and

conditions of

can't solve the faults of

OBU

in the

process of service,

needs to cooperate with other

maintenance companies that should meet th e condition

 

2 4 ?Automobile Sales ServicshopL S=

. If

satisfies the

above conditions, SSC will convert the ciphertext under

condition to the re-encrypted ciphertext under

condition,

so that

can decrypt the re-encrypted ciphertext.

OBU

RSU

Consortium blockchain SSC

Generate the key Generate the key

Generate the

re-encryption

key

The search

request

Send the

ciphertext

Send the

ciphertext

Record and store

the ciphertext

Send the re-

encryption

ciphertext

FIGURE 4. Secure data sharing

The process of secure data sharing is divided into two

stages. The first stage is that the

OBU

, as the data owner,

sends a search request to SSC, and SSC feedbacks retrieval

results to the vehicle maintenance company

. The second

stage is that maintenance company

, as the data owner,

sends a search request to SSC, and SSC feeds search results

back to maintenance company

In this paper, we propose a searchable attribute-based proxy

re-encryption algorithm, which not only implements secure

data sharing and keyword search, but also data forwarding.

Table 1 is the symbol description involved in our scheme.

Take the automobile maintenance as an example to describe

our scheme in detail. The process of providing services by the

insurance company and the traffic police is similar, and is not

repeated here.

TABLE 1. NOTATION DESCRIPTIONS

Notation

Definition

Sparams

System parameters

MSK

Master screte key

Secret key

Re-encryption key

Attribute set

User’s attribute set

Keyword set

( , )M



Shared Permission

Plaintext

Ciphertext

Re-encrypted ciphertext

A. SYSTEM INITIALIZATION

and

are two multiplicative cyclic groups with

prime order

and

are generators of

. There is a

bilinear pairing

1 1 2

:e G G G→

. Define message

authentication function

and six target collision resistance

hash functions:

 

1: 0,1 k



→

 

: 0,1 k

HG→

 

3 4 5 1

, , : 0,1H H H G

→

 

6: 0,1 k



→

. Random select

a b Z



, input security parameter



and the attribute

universe

, output the system

parameters

( )

1 1 2 3 4 5 6

, , , , ( , ) , , , , , , , ,

Sparams e p g g e g g g Y H H H H H H=

and

the master key

( )

MSK g a=

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Access

B. KEY GENERATION

Key generation includes private key generation and search

key generation.

(1) PRIVATE KEY GENERATION

Input system parameter

Sparams

and the user’s attribute

set

SX

with identity tag

, randomly select





calculate

b ac

A g g=

Bg=

, and

 

3()

xxS

D H x 

. Generate

private key

( )

,,x

SK A B D=

for the user.

stores

( , )

in the local list.

(2) SEARCH KEY GENERATION

When the user searches the keyword

is randomly

selected from

to calculate



searches

the local list after receiving

( )



, and if

is in the local list,

will generate a search key

'ac b

SK g



for the keyword.

C. DATA ENCRYPTION

in the access structure

( , )M



is a matrix of

ln

represents the

j th−

row vector of



is the row

mapping of

OBU

randomly selects

 

0,1 k





calculate

1( , )s H m



. The vector

( , , , , )

z s z z z=

selected from

Z

to share the secret index

, then randomly

select

,, lp

r r r Z



and calculate

 

 

 

1 2 2 3 1

[]

4 1 3 1

( || ) ( ( , ) ), ,

( ( ))

( ( , ,( , ) ,( , )))

bs s s

jjl

j j j

U m H e g g U g U g

V g H j

Z H U U V W M







−



=  = =

=



=



=



(3)

Where



=

 

( ) |1J j S j l



=   

represents the

attribute used in the access structure

( , )M



, and

is the

number of attributes in the access structure

( , )M



OBU

sends ciphertext

( )

 

1 2 3

, , , , ,

jj jl

C U U U V W Z 

RSU

records the ciphertext

in the block and uses the Ripple

consensus to verify the validity of the data in the block. Once

the verification is successful, the current block is connected to

the blockchain.

D. RE-ENCRYPTION KEY GENERATION

randomly selects

 

, 0,1 k





, calculates

1( , )sH



selects vector

' ' ' ' '

( , , , , )

z s z z z=

from

Z

sharing the

secret index

. Let



=

, where

is the

j th−

row vector of

in the new access structure

( , )M



(

is a matrix of

ln



is row mapping of

). Randomly

select

' ' '

,, lp

r r r Z



and calculate

 

' ' '

1 2 2

[]

' ' ' ' ' ' '

5 1 2 1

( || ) ( ( , ) ),

( ( ))

( ( , ,( , ) , ,( , )))

bs s

jjl

j j j

U H e g g U g

V g H j

Z H U U V W S M







−







=  =



=





=



=





(4)

Output

( )

' ' ' ' '

4 1 2

, , , ,

RK U U V W Z=

. Randomly select



from

Z

and calculate

 

()

2()

()

xxxS

RK A g

RK g

RK B









=

=



=





=



(5)

sends the re-encryption key

1 2 3 4

( , , , , )

RK RK RK RK RK R=

to the SSC.

E. RE-ENCRYPTION

Suppose there are coefficient

 







such that

( )

1,0, ,0

jJ M



=



, then

jJ s



=



. After receiving

the re-encryption key

, SSC first verifies whether the

re-encryption key contains a valid attribute set

and the

access structure

( , )M



, namely to check whether the

verification equation (6) is valid or not.

' ' ' ' ' ' ' '

2 5 1 2 1 1

( , ( , ,( , ), ,( , ))) ( , )e U H U U V W S M e g Z



(6)

When equation (6) is true, the SSC verifies the validity of the

ciphertext, that is., whether equation (7) holds.

2 1 3

3 4 1 3 1 1 1

( , ) ( , )

( , ( , ,( , ) ,( , ))) ( , )

( , ) ( , ) ( , ( ( )) )

j J j J

e U g e g U

e U H U U V W M e g Z

e V g e U g e W H j





−



=



=



=





(7)

If equation (7) is valid and then calculate

2 1 3 2

3 ( )

( , ) / ( , )

( ( , ) ( , )) j

j j j

e U RK e U RK

Ue V RK e W R







=



(8)

Re-encryption key

is used to encrypt the ciphertext

and re-encryption ciphertext is

'1 2 3 4 4 1

( , , , , ,( , ) , , ,( , ))

j j j

C U U U U RK V W Z S M



F. INDEX AND SEARCH TOKEN GENERATION

The keyword set of plaintext

 

KW kw =

, and a

bit string

is randomly selected for each keyword. The

authentication code

( , ) ( , ( ))

bs s

y e g g e g H kw=

the ciphertext C is calculated, and the index of ciphertext is

( )

, ( , )

j j j

Index h Y y h=

. Similarly, the authentication code

( , ) ( , ( ))

bs s

y e g g e g H kw=

in re-encrypted ciphertext

obtained, and the index of re-encrypted ciphertext is

( )

, ( , )

j j j

Index h Y y h=

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Access

According to the user’s private key, the attribute set, the

keyword

and the corresponding search key

, we can

calculate

( )

 

ac b

I H kw g





=



=



=





(9)

So the search token of keyword

( )

,,x

tk I B D=

, and

( )

,,x

tk I B D=

is stored in the SSC, which is convenient to

provide keyword search service for users.

G. VERIFICATION

In order to retrieve keyword, the user sends search token

and the attribute set to SSC. The user can perform keyword

search on the ciphertext or keyword search on the

re-encrypted ciphertext.

Step 1: After receiving the user's search token

and the

attribute set

, the SSC verifies whether the attribute set

satisfies the access structure

( )



. If so, calculate

( )

, ( , )

( , )

C j j j

kw C

Q e V B e W D

e U I







=





=







(10)

Step 2: Verify whether the keyword

is the

same as that in

Index

, that is to verify whether equation (11)

is true or not.

( ) ( )

j kw j j

Y h O Y h y=

(11)

Step 3: If the equation (11) is true, the SSC sends the

retrieved ciphertext to the user, otherwise output

⊥

The process of searching re-encrypted ciphertext is similar.

First, verify whether the attribute set

satisfies the access

structure

( )



. If it is satisfied, calculate

( )

'' ' ' '

, ( , ) j

CjJ

Q e V B e W D







=



and

( , )

e U I

. Then

verify whether the keyword

is the same as that in

Index

. If that is the same, the retrieved re-encrypted

ciphertext is sent to the user, otherwise output

⊥

H. CIPHERTEXT DECRYPTION

The user who obtains the ciphertext first verifies the

validity of the ciphertext according to equation (7), outputs

⊥

if the verification fails, otherwise calculates

( , )

e U A

( )



, then calculate

( )

||m H R U



=

to get

plaintext, otherwise output

⊥

I. RE-ENCRYPTION CIPHERTEXT DECRYPTION

Let

 

' ' ' '

( ) |1J j S j l



=   

, verify whether

' ' ' ' ' ' ' ' '

2 5 1 2 1

( , ( , ,( , ) , ,( , ))) ( , )

j j j

e U H U U V W S M e g Z



is true, output

⊥

if the equation is invalid, otherwise calculate

( , )

e U A

. If

( )



( )

()

( )

4 1 3 1

, , , , , Hm

Z H U U V W M





，then calculate

( )

2 4 1

|| H

m H U U





=

to get plaintext, otherwise output

⊥

Ⅴ. CUSTOMIZED SERVICES

A. INSURANCE CLAIM SETTLEMENT AND PRICING

CPSC structure as shown in Figure 5. There are three kinds

of liability attributions for traffic accidents. Personal liability

refers to the situation that the owner violates traffic rules,

such as speeding, emergency braking, etc. Commodity

liability refers to the condition in which the accident occurs

due to the defective devices yielded by the manufacturer.

Service liability refers to the scene where bugs in software

provided by software providers cause accidents. The

insurance company arbitrates according to the responding

traffic data after the accident happens, and then automatically

compensates the injured party based on the arbitration result.

CPSC not only changes the long waiting period of existing

insurance claims, but also avoids insurance fraud.

Insurance companies obtain the information of mileage,

speed, and acceleration from traffic data. Data analysis

technology is used to establish the driving style evaluation

model, which analyzes vehicle owners’ driving behaviors and

habits. Then the insurance company provides the personalized

insurance pricing aligned with their driving styles to users.

Vehicle owners must pay exorbitant insurance if their mileage,

speed and acceleration exceed the threshold, and vice versa,

only pay the normal price of insurance. Compared with

traditional car insurance pricing, personalized insurance

pricing promotes car owners to correct bad driving habits,

improves traffic safety and reduces insurance costs.

Speed

Harsh

brake

ABS

Mileage

Accele-

ration

Defective

part

Decryption

Personal liability

Driving style

evaluation

Insurance pricing

Commodity liability

Service liability

Software

bug

Consortium blockchain

FIGURE 5. CPSC structure diagram

B. TRAFFIC VIOLATION PENALTY

Figure 6 shows the flow chart of VPSC. The traffic police

decrypts the speed, location and lane change from the

consortium blockchain. When the vehicle has the violations of

speeding, retrograde, or illegal lane change, the traffic police

deducts vehicle scores and fines. If the remaining scores are

insufficient, the vehicle owner must take driver’s theoretical

test again. The vehicle owner has got to pay fines within the

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Access

prescribed time, otherwise the late fee will arise. In case fines

and late fees are not paid in the specified time, the vehicle

owner is added to the blacklist. VPSC promotes car owners to

restrain their driving behavior, and effectively curb the

phenomenon of “buying and selling scores”, that is, car

owners with deficient scores buy scores from other car

owners with sufficient scores to avoid the driver’s theoretical

examination.

Start

Traffic police

Decrypt and

obtain data

End

Pay all expenses in

the set time

Pay fines in the set

time

Sufficient

scores

Vehicle

violation

Generate late

fees

Deduct scores

Take drivers

examination

Punished by

the blacklist

FIGURE 6. VPSC flow chart

C.MAINTENANCE SERVICE

Automobile faults can be divided into minor faults, general

faults and fatal faults, as shown in Figure 7. In case of a fault,

OBU

immediately transmits a fault report to the maintenance

service provider, which includes fault types, fault components,

location, fault time, OBU’s

, and the vehicle owner’s

contact number. The maintenance service provider leverages

the MSSC to make maintenance strategies based on failure

reports. For instance, software updating only needs remote

control. Nevertheless, the maintenance service provider sends

a general repair notice to the owner under the circumstances

of uneven tire wear and lighting damage. Furthermore, when

the vehicle has steering wheel malfunction, engine power loss

or other fatal failures, the maintenance service provider sends

the warning message to the owner and makes an appointment

for on-site repairs.

Software

update

Auto faults

diagnosis

Minor

faults

General

faults Fatal

faults

Tire

wear

Lighting

damage Brake

wear Engine

damage

Throttle

wear

Steering

Wheel

fault

Software

fix

FIGURE 7. Automobile faults classification

Ⅵ. SECURITY ANALYSIS AND PERFORMANCE

EVALUATION

In this section, we describe the security analysis and

performance evaluation of our proposed DSCSCB. The

experimental results show that our scheme has better

performance.

A. SECURITY ANALYSIS

(1) Correctness for data

1) Correctness for ciphertext

Simplify

to get

( )

()

( ) ( )

( )

()

( )

, ( , )

( ( ( )) , ) , ( )

= ( ( )) , , , ( )

= ,

j j j j

j j j j j

j j j

C j j j

a r c d r

r c d a c d r

ac d

acs d

Q e V B e W D

e g H j g e g H j

e H j g e g g e g H j

e g g















−



−



=





(12)

is simplified as

( )

( ) ( )

s b ac bs

d acs

e g g g

e U A

R e g g

Qe g g

= = =

(13)

Then

( ) ( )

( )

2 1 2 2

, ( || ) ( ( , ) )= ||

bs bs

H R U H e g g m H e g g m



 =  

so the ciphertext decryption is correct.

2) Correctness for re-encrypted ciphertext

( )

',bs

R e g g=

can be obtained from correctness for

ciphertext and

is simplified as

( )

()

( )

()

( )

()

( )

()

( )

2 1 3 2

3 ( )

()

( , ) / ( , )

( ( , ) ( , ))

=( ( )) , ,

=, ( ( )) , ,

j j j

s b ac s

HcH

a r r

bH acH

a r r

cH cH

e U RK e U RK

Ue V RK e W R

e g g g g e g g

e g H j g e g H j

e g g e g g

e g g e H j g e g H j

e g g

























−



−



=







( )

acH

acsH

bsH

e g g







(14)

Then

( )

2 4 1 2 2

, ( || ) ( ( , ) )= ||

Hbs

H U U H e g g m H e g g m





 =  





so the decryption process of re-encrypted ciphertext is correct.

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Access

(2) The security of data

1) The security of ciphertext

The validity of the ciphertext is verified by equations

2 1 3

( , ) ( , )e U g e g U=

and

3 4 1 3 1 1 1

( , ( , ,( , ) ,( , ))) ( , )

e U H U U V W M e g Z



, where

4 1 3 1

( ( , ,( , ) ,( , )))

j j j

Z H U U V W M



can be regarded as a

signature. We use unidirectionality and collision resistant hash

functions to prevent

，

( , )l

j j j

VW =

and the access

structure

( , )M



from being tampered with. Besides, the

integrity of

is bound by

. If any of

, and

( , )l

j j j

VW =

in the ciphertext is forged or tampered with, the Eq.

(7) will not hold. Therefore, the security of ciphertext is

guaranteed.

2) The security of re-encrypted ciphertext

The validity of the attribute set

and the access structure

( , )M



are verified by the equation

' ' ' ' ' ' ' '

2 5 1 2 1 1

( , ( , ,( , ), ,( , ))) ( , )e U H U U V W S M e g Z



before

re-encrypting the ciphertext. The equation

( , ) ( , ) ( , ( ( )) )

j J j J

e V g e U g e W H j





−



=



guarantees the

security of

( )

VW =

. The security of

( , )l

j j j

VW =

and the access structure

( , )M



is guaranteed by the

signature

. Furthermore, the validity of

is verified by

the equation

2 1 3

( , ) ( , )e U g e g U=

. Clearly,

is the part of

the re-encryption key and its security is guaranteed by the

trusted authority. Hence the re-encrypted ciphertext

'1 2 3 4 4 1

( , , , , ,( , ) , , ,( , ))

j j j

C U U U U RK V W Z S M



is secure and

effective.

3) The security of keywords

The authentication code

( , ) ( , ( ))

bs s

y e g g e g H kw=

is the

result of encrypting the keyword

. It is almost impossible

to deduce the keyword from the authentication code. Even

though

( )

e g g

is obtained,

1( , )s H m



cannot been

known. Thus no information of the keyword can be obtained.

Similarly, the keyword of the re-encrypted ciphertext are also

secure. Users must send a search token to the smart contract

before performing keyword search. In addition, each keyword

corresponds to different

, which further improves the

concealment and security of the keyword.

(3) Collusion resistant

The attribute set

and the access structure

( )



are

verified by

. Besides,

, and

are closely

related to

through



. However,

is closely

related to

through



. As long as any of

, and

is tampered with, the re-encrypted ciphertext is

invalid. If the attribute set, the access structure, and

are

tampered with, the equation

' ' ' ' ' ' ' '

2 5 1 2 1 1

( , ( , ,( , ), ,( , ))) ( , )e U H U U V W S M e g Z



will not hold.

Therefore our algorithm successfully resists collusion attacks.

(4) The security of consortium blockchain

The trusted authority only generates the private key and the

search key for users who have the identity tag

in the local

list. That is to say, only users who satisfy the access structure

can perform keyword search, proxy re-encryption and secure

data sharing in the entire consortium blockchain network.

Search requests that do not satisfy the access structure will be

ignored, which will not only ensure the security of the

blockchain network, but also reduce the communication

overhead and computational overhead to a certain extent.

Users with different attribute sets have different identity tags.

This identity tag is only used to distinguish the user's identity

and does not reveal the user's identity privacy.

The

OBU

needs to pay the credit value as a collateral

when it requests customized services, which avoids false

requests and replay attacks. After the service sector provides

customized services, the smart contract automatically deducts

the credit value as the service fee and then returns the

remaining credit value to the

OBU

. When the service sector

needs relevant data to analyze and predict the customer's

habits, so as to provide better services for customers, the

service sector will reward the credit value to the

OBU

providing the data, which promotes secure data sharing.

Our scheme uses Ripple consensus to verify the data.

Assuming there are

verification nodes in the network and

the probability that the verification nodes become malicious

nodes is

. Data cannot be tampered with unless there are

at least

f−

malicious nodes in the network. Thereby the

probability of successfully tampering with the block is

( )

12f−

. For instance, if there are 201 verification nodes in

the network, the probability of successfully tampering with

the block is

40 13

1 2 9.095 10−



. Therefore the data in the

block is almost impossible to be tampered with.

B. PERFORMANCE EVALUTION

The

OBU

encrypts the data and the access structure to

generate the ciphertext and sends it to the

RSU

. The

RSU

records the ciphertext into the block, and the verification node

verifies the data in the block. After the verification succeeds,

the block is connected to the blockchain. The search service

smart contract will send the ciphertext to the service sector

that satisfies the access structure. The service sector receives

the corresponding ciphertext for decryption, and provides

customized services to the

OBU

. For example, the insurance

company designs appropriate insurance pricing and automatic

claim settlement services for the

OBU

. The traffic police

automatically deducts credit value and fines for the

OBU

that violates traffic rules, so as to regulate the driving

behavior of the vehicle owner. The vehicle maintenance

provides maintenance services for the fault vehicle. In the

process of providing services, if the service sector needs to

cooperate with other companies in the same field, keyword

search can be carried out. The company that satisfies search

keywords and the access structure will receive re-encrypted

ciphertext. Then the company decrypts the re-encrypted

ciphertext to obtain the corresponding plaintext, providing

high quality and efficient services for the

OBU

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Access

TABLE 2. Performance comparison between our scheme and other schemes

Performance

Ref. [41]

Ref. [42]

Ref. [43]

DSCSCB

Confidentiality

✓

Anti-collusion

✓



✓

Data fine grain

management



✓

Multidimensional

service



✓

Tamper-proofing

✓

Decentration



✓

Table 2 evaluates the performance of the existing schemes

and our proposed DSCSCB. We propose an attribute-based

proxy re-encryption algorithm that supports keyword search

to realize secure data sharing and proxy re-encryption, which

is beneficial to data fine-grained management and access

control, protects data confidentiality and security, and can

resist collusion attacks. Service sectors use the acquired data

to provide multi-dimensional and customized services for the

OBU

. We use the consortium blockchain technology to break

the centralized structure in the traditional intelligent

transportation. The chain structure and the Ripple consensus

effectively prevent data from being tampered with. Table 2

shows that our scheme has better performance than other

schemes and is more suitable for secure data sharing and

customized services for intelligent transportation.

C. COMPUTATIONAL OVERHEAD

The computational overhead mainly includes encryption,

re-encryption, decryption and re-encrypted ciphertext

decryption (re-decryption). Table 3 shows the comparison

results of our scheme with references [44], [45] and [46],

where

is the bilinear operation,

is the exponential

operation on the multiplicative cyclic group. Compared with

the above two operations, the multiplication operation’s

computation cost is very small and can be ignored.

represents the number of attributes in the access structure and

represents the number of attributes satisfying the access

structure. The experiment runs on the Intel i5 processor with

8G memory and 3.0GHz frequency. The above two

operations consume 1.57ms and 0.311ms respectively.

TABLE 3. Comparison of the computational overhead

Scheme

encryption

Re-encryption

decryption

Re- decryption

Ref. [44]

( )

3 4 2

l T T++

( ) ( )

3 5 1

J T J T+ + +

( )

J T T++

( )

J T T++

Ref. [45]

( )

l T T++

( ) ( )

3 10 5 2

J T J T+ + +

J T T+

( )

J T T++

Ref. [46]

( )

3 6 2

l T T++

( ) ( )

11 17 2 7

J T J T+ + +

( )

T J T++

( )

2 2 7

T J T++

Our scheme

( )

lT+

( )

JT+

( )

1EB

J T T++

( )

J T T++

Figure 8 shows a comparison of computational overhead.

Figure 8(a) shows that the computational overhead increases

linearly with the number of attributes in the data encryption

process. We use the hash function to sign the ciphertext.

However, in references [44], [45] and [46], there is only data

encryption and no signature process. Our scheme not only

protects the integrity and non-repudiation of data, but also

takes less time.

Figure 8(b) shows that the computational overhead of

re-encryption, which increases linearly with the number of

attributes. Before performing re-encryption, we first verify

whether the re-encryption key contains a valid attribute set

and the access structure, and then verify the validity of the

ciphertext. If any verification process fails, we discard the

data and terminate re-encryption. Our scheme has certain

advantages over other schemes. Ref. [44] defines a parameter

with complex calculation, which costs too much. In the

process of re-encryption in Ref. [45], the proxy needs to

frequently verify the user’s token, resulting in a large

computational overhead.

Figure 8(c) shows that as the number of attributes increases,

the computational cost of our scheme in the decryption

process is the least. The search service smart contract

simultaneously performs keyword matching and ciphertext

partial decryption in the verification stage, which greatly

reduces the computational overhead of decryption by the

service sector. Our scheme only needs 17.431ms for

ciphertext decryption with 50 attributes. Compared with the

other three schemes, the computational overhead is reduced

by 47.08% on average. In Ref. [46], the bilinear operation

with large computational cost is frequently used in the

decryption process, so the calculation overhead of the

ciphertext decryption is the largest.

Figure 8(d) shows that the computational cost of

re-decryption is linearly related to the number of attributes.

As the number of attributes increases, our scheme advantages

are more obvious. This scheme verifies whether the attribute

set and access structure have been tampered with by the

equation

' ' ' ' ' ' ' ' '

2 5 1 2 1

( , ( , ,( , ) , ,( , ))) ( , )

j j j

e U H U U V W S M e g Z



before decrypting the re-encrypted ciphertext. Once the

re-encrypted ciphertext is tampered with, the search service

smart contract discard it, which reduces the network burden

and computational overhead. Our scheme only needs

20.882ms to decrypt the ciphertext with 50 attributes.

Compared with the other three schemes, the computational

overhead is reduced by 45.35% on average.

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Access

010 20 30 40 50

Computational overhead (ms)

Number of attributes

Ref. [44]

Ref. [45]

Ref. [46]

Our scheme

010 20 30 40 50

100

200

300

400

500

Computational overhead (ms)

Number of attributes

Ref. [44]

Ref. [45]

Ref. [46]

Our scheme

(a) Data encryption (b) Re-encryption

010 20 30 40 50

120

150

180

Computational overhead (ms)

Number of atttibutes

Ref. [44]

Ref. [45]

Ref. [46]

Our scheme

010 20 30 40 50

120

150

180

Computational overhead (ms)

Number of attributes

Ref. [44]

Ref. [45]

Ref. [46]

Our scheme

FIGURE 8. Computational overhead comparison diagram

D. COMMUNICATION OVERHEAD

Suppose that

and

represent the bit length of

and

respectively, which are 60bit and 40bit. The length

is very small and can be ignored.

represents the

number of user attributes,

represents the number of

attributes in the access structure, and

represents the

number of attributes satisfying the access structure. The

communication overhead in the process of secure data sharing

and customized services provided by the service sector

mainly includes system parameters, the private key, the

ciphertext and the re-encrypted ciphertext. Table 4 shows the

comparison results of communication cost between our

scheme and references [44], [45] and [46].

TABLE 4. Comparison of the communication overheads

Scheme

System parameters

The private key

Ciphertext

Re- encrypted ciphertext

Ref. [44]

10 3GG+

( )

21SG+

( )

25lG+

( )

6 10J G G++

Ref. [45]

5GG+

( )

23SG+

( )

23l G G++

( )

43J G G++

Ref. [46]

82GG+

( )

24SG+

( )

25l G G++

( ) ( )

2 7 2J G J G+ + +

Our scheme

6GG+

( )

2SG+

( )

24lG+

( )

61J G G++

In the process of system parameters generation,

1 3 4 5 1

, , , , ,

g g g H H H G

and

( , )b

e g g G

. So the

communication overhead of system parameters is

6 6 60 40 400G G bit+ =  + =

. During private key generation,

,A B G

and

DG

(

xS

). So the communication

overhead of the private key is

( ) ( )

2 60 2S G S+ = +

. In the

ciphertext,

1 2 3 1

, , ,U U U Z G

and

V W G

(

jl

), so the

communication cost of the ciphertext is

( ) ( )

2 4 60 2 4l G l+ = +

. The calculation process for the

communication overhead of re-encrypted ciphertext is similar,

which will not be described again here.

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Access

150

300

450

600

750

900

400

560

380

720

Our scheme

Ref. [38]Ref. [45]

Ref. [44]

Communication overhead (bit)

010 20 30 40 50

1000

2000

3000

4000

5000

6000

7000

Communication overhead (bit)

Number of attributes

Ref. [44]

Ref. [45]

Ref. [46]

Our scheme

(a) System parameters (b) The private key

010 20 30 40 50

1300

2600

3900

5200

6500

Communication overhead (bit)

Number of attributes

Ref. [44]

Ref. [45]

Ref. [46]

Our scheme

0 10 20 30 40 50

5000

10000

15000

20000

Communication overhead (bit)

Number of attributes

Ref. [44]

Ref. [45]

Ref. [46]

Our scheme

FIGURE 9. Communication overhead comparison diagram

Figure 9 shows the comparison of communication overhead

between this paper and other three schemes. Figure 9 (a) and

(b) show that the communication overhead of system

parameters and the private key in our scheme has obvious

advantages over the other three schemes. Figure 9 (c) shows

the communication overhead of ciphertext. In our scheme,

ciphertext contains the signatures, so the communication

overhead is slightly larger than that of Ref. [45], but it has

certain advantages compared with Ref. [44] and Ref. [46].

The communication overhead of the re-encrypted ciphertext is

shown in Figure 9 (d). Compared with references [45] and

[46], our scheme has more communication overhead. The

re-encrypted ciphertext contains the parameter

with

large communication overhead. This parameter can prevent

the user’s attribute set and the access structure from being

tampered with, and solve the unverifiability of the

re-encrypted ciphertext in Ref. [45]. In addition,

( )

VW =

and

( )



are signed by

, so their validity can

be guaranteed. However, Ref. [46] cannot guarantee the

validity of the components in the re-encrypted ciphertext.

Therefore, the communication overhead of the re-encrypted

ciphertext in this paper is greater than that in Ref. [45] and

Ref. [46].

E. CONSORTIUM BLOCKCHAIN DELAY

The Ripple consensus used in this paper can generate a new

block in only 3-6 seconds and do not need any confirmation

time, so it only takes 3-6 seconds to generate a valid block.

However, the Delegated Proof of Stake (DPoS) consensus

generates a new block every 2 seconds, requiring 12 seconds

of confirmation time. The Proof of Work (PoW) consensus

generates a block every 10 minutes, requiring 60 minutes of

confirmation time. Compared with DPoS and PoW, the Ripple

consensus generates blocks and confirms data faster, so the

delay is less.

Ⅶ. CONCLUSION

This paper proposed a novel scheme of secure data sharing

and customized services for intelligent transportation based

on the consortium blockchain, which not only conquers the

disadvantage of the centralized data management in

traditional intelligent transportation, but also ensures the

confidentiality and security in the data interaction process,

and thus effectively resists collusion attacks. The proposed

attribute-based proxy re-encryption algorithm has the function

of keyword searching by dividing the key into the attribute

key and the search key, which not only supports keyword

retrieval and proxy re-encryption, but also realizes secure data

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.

Access

sharing, and then prevents the privacy data leakage of the

OBU

. After that, service sectors can use the smart contract to

provide convenient and customized services, such as

insurance pricing, vehicle maintenance, etc. Security analysis

and performance evaluation show that our scheme has

obvious advantages in the aspects of security, computational

overhead, communication overhead and delay. Therefore, our

scheme is suitable for secure data sharing and customized

services in intelligent transportation.

In future research, we aim to propose an algorithm with

better performance and less computational cost and

communication overhead.

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Sep 2023

Timothy Murkomen

Integrating blockchain technology into vehicular ad hoc networks (VANETs) introduces crucial considerations related to reliability, efficiency, and transparency. As distributed ledger technology (DLT) continues to reshape VANET communication models, this systematic survey provides a comprehensive categorization of blockchain-enabled applications within VANET domains. The review delves into the advancements of blockchain and VANETs through analytical exploration and survey, shedding light on the techniques and limitations of blockchain deployment in VANETs for a robust decentralized network implementation. The study carefully examines existing research to offer an extensive overview of blockchain in VANETs. Blockchain and VANETs applications are gaining traction across various industrial sectors, attracting both researchers and practitioners. The research aims to identify and address open issues related to blockchain integration with VANETs, paving the way for future prospects. Moreover, the paper explores the comprehension of blockchain applications within the Internet of Vehicles (IoV) to bridge research gaps in advanced communication networks within the broader Internet of Things landscape. In summary, this survey paper contributes valuable insights about techniques and models used in various blockchain primary areas in the context of VANETs.

Automation of Financial Aid Distribution Systems Using Consortium Blockchain

Conference Paper

Mar 2024

Aid Nexus : A Blockchain-Based Financial Distribution System

Chapter

Mar 2024

Blockchain technology has emerged as a disruptive force with transformative potential across numerous industries, promising efficient and automated solutions that can revolutionize traditional systems. By leveraging decentralized ledger systems, blockchain offers enhanced security, transparency, and transaction verification without the need for intermediaries. The finance sector is exploring blockchain-based solutions for payments, remittances, lending, and investments, while health care adopts the technology for medical record keeping, supply chain tracking, and data management. Similarly, supply chain management benefits from blockchain’s ability to enhance transparency, traceability, and accountability from raw materials to finished products. Other sectors, including real estate, energy, and government, are also investigating blockchain-based solutions to improve efficiency, security, and transparency. Furthermore, smart contracts within the blockchain enable process automation, reducing manual intervention in distribution workflows. AidNeux, a consortium-based blockchain DApp, reimagines the distribution of financial assistance by addressing inefficiencies and opaqueness. Using smart contracts ensures the security and directness of money transfers. Its robust digital identity verification and real-time auditability reduce fraud risks and strengthen accountability, thereby presenting a scalable, transparent solution to problems inherent to conventional financial aid systems.

A New Paradigm in Blockchain-Based Financial Aid Distribution

Chapter

Feb 2024

Blockchain technology has emerged as a game-changer in a variety of industries, providing robust solutions that can supplant conventional procedures. The unique potential of this technology originates from its decentralized ledger systems, which enable enhanced security, transparency, and the validation of transactions without the need for intermediaries. Notably, the financial sector is making substantial progress toward implementing blockchain solutions for a variety of operations, including remittances, lending, and investments. The healthcare industry is simultaneously incorporating this technology into systems for managing medical records, tracing supply chains, and data management. Similarly, the capacity of blockchain to enhance transparency, traceability, and accountability is widely acknowledged in supply chain management, from the procurement of basic materials to the delivery of finished goods. Diverse industries, including real estate, energy, and government, are actively investigating the potential of blockchain to improve efficiency, security, and transparency. Notably, Hyperledger Besu, an open-source blockchain platform, is used to implement smart contracts that automate processes and reduce manual intervention along distribution pathways. This exhaustive review examines the transformative potential of blockchain technology across a variety of industries, discussing the obstacles encountered and providing key insights into future research and development directions. This paper seeks to serve as a pivotal resource for academics, industry stakeholders, and policymakers by synthesizing existing scholarly literature and shedding light on significant findings.

A Survey on Sharing Cloud Data Securely with Encrypted Indexing and User Identity Verification

Conference Paper

Dec 2023

Study and Analysis of various Privacy Preserved Data Sharing Framework in E-Government System based on Consortium Blockchain: A Challenging Overview

Conference Paper

Dec 2023

Survey on Secure Keyword Search over Outsourced Data: From Cloud to Blockchain-Assisted Architecture

Article

Aug 2023

Secure keyword search is a prevailing search service offered in outsourced environments. However, with the increasingly severe security vulnerabilities of conventional centralized outsourcing, the architecture of secure keyword search, with searchable encryption (SE) as the underlying technique, has recently shifted from cloud-centered models to blockchain-assisted models. Existing surveys commonly fail to capture such an evolution and the corresponding benefits. What on earth does blockchain bring about and what are the unexplored challenges? This survey provides a systematic review of secure keyword search over outsourced data from cloud to blockchain-assisted architectures. We propose a taxonomy assorting present studies, depending on whether cloud/blockchain and data sharing are included, in which blockchain-assisted architecture is further divided into blockchain-side and cloud-side keyword search, respectively. Technically, we conclude five types of representative SE techniques with fitting architectures, either cryptographic-based or hardware-dependent. Notably, we propose comprehensive methodologies to select relevant papers, discuss, and compare existing schemes regarding functionalities, security, efficiency, and fairness (up to 21 compared items). Finally, open issues and potential research directions are identified for future work. We aspire to help pave the way for addressing the theoretical and empirical aspects of secure keyword search and full-fledged real-world implementation of blockchain-based keyword search applications.

Charging Guiding Strategy for Electric Taxis Based on Consortium Blockchain

Article

Full-text available

Oct 2019

The issues in charging guiding for electric vehicles are meaningful studies in recent years, especially for electric taxis which need to be recharged during working hours. However, with the popularity of taxi-booking apps, how to obtain an effective charging guiding for taxis having advance orders becomes an urgent problem to be solved. To optimize various special interests while satisfying the constraints of online advance orders, a charging guiding strategy based on consortium blockchain is proposed in this paper. Firstly, a taxi charging guiding architecture based on consortium blockchain is designed, and an improved practical byzantine fault tolerance algorithm is proposed to solve the problem of charging information disconnection and trust between multiple charging station operators. Secondly, we establish the charging guiding model for electric taxis based on multi-objective optimization. The model aims to meet the constraints of online advance orders, maximize passengers’ satisfaction and operators’ service efficiency, and minimize the charging costs of taxis. Finally, the optimization model is solved by quantum-behaved particle swarm optimization. In order to verify the effectiveness of the proposed guiding strategy, main urban areas of a city are taken as examples for simulation. The results show that the proposed strategy has increased the passengers’ satisfaction by 0.44%, and has decreased the expense cost, the time cost and distance cost by 2.38%, 5.72%, and 17.25% respectively, comparing with PSO based strategy while balancing the utilization of charging equipment.

Adaptive Traffic Signal Control Mechanism for Intelligent Transportation Based on a Consortium Blockchain

Article

Full-text available

Jul 2019

The development of vehicular ad-hoc networks (VANETs) has facilitated adaptive traffic signal control for intelligent transportation. In this paper, we proposed the traffic signal control mechanism based on a consortium blockchain, which has saved plenty of financial and material resources. It has solved the centralization problems and minimized the high degree of human intervention in the process of traffic signal light management. As a road is congested, the vehicle forwards road condition messages. The traffic department ( $TD$ ) adjusts the signal light duration to allow the synergistic optimization management, and control the traffic vehicle status through a smart contract. In addition, we propose a credibility mechanism to effectively prevent vehicles from broadcasting mendacious messages and malicious requests, thereby enhancing the credibility of vehicles and providing a secure and trustworthy communication environment for the VANETs. It is hazardous for vehicles to send plaintext messages in an open environment because their privacy and security are threatened. Thus, we utilize ElGamal encryption and group signature algorithm to guarantee the confidentiality, privacy, and non-repudiation of any information. The safety analysis and performance evaluation demonstrate that the scheme is feasible and valid, and it can facilitate the adaptive control of traffic signal lights.

SAPSC: SignRecrypting authentication protocol using shareable clouds in VANET groups

Article

Full-text available

Jun 2019
IET INTELL TRANSP SY

Security and mutual reliability are the crucial requirements of an ad hoc network as nodes are dependent upon each other for routing and forwarding their messages. Vehicular ad hoc networks (VANETs) are no exception and are always at the risk of impersonation attack. An authentication protocol protects the identity of network entities from being impersonated. Occasionally, they require re‐encryption technique which enables any other node to communicate on behalf of an unavailable node. The technology is used to provide backup in emergencies. We propose a secure authentication algorithm to efficiently re‐encrypt the messages using signcryption. For faster computation and routing, the authors have used shareable clouds in VANET groups. Security of the protocol is proved using Burrows–Abadi–Needham logic and validated by simulation tool automated validation of internet security protocols and applications.

A Review of Security Aspects in Vehicular Ad-Hoc Networks

Article

Full-text available

Mar 2019

In recent years, cooperative systems have been gaining relevance in autonomous driving, to such an extent that they are necessary to reach the final goal, the autonomous car. But the relevance that vanet have acquired shows that it is still an immature technology and needs more time to develop. Proof of this is the most of these systems do not provide real security in communications. So the scientific community has tried to develop new technologies to allow a greater security in this type of communications. Therefore, in this article a review of how the technology has evolved to maintain safety in the vanet will be carried out. In addition, a breakdown of the different security technologies grouped by their type and the advantages and disadvantages of each one.

Blockchain-Based Traffic Event Validation and Trust Verification for VANETs

Article

Full-text available

Mar 2019

Sharing traffic information on the vehicular network can help in the implementation of intelligent traffic management, such as car accident warnings, road construction notices, and driver route changes to reduce traffic congestion earlier. In the future, in the case of autonomous driving, traffic information will be exchanged more frequently and more immediately. Once the exposed traffic incident is incorrect, the driving route will be misleading and the driving response may be in danger. The blockchain ensures the correctness of data and tamper resistance in the consensus mechanism, which can solve such similar problems. This paper proposes a Proof-of-Event consensus concept applicable to vehicular networks rather than Proof-of-Work or Proof-of-Authority approaches. The traffic data are collected through the roadside units and the passing vehicles will verify the correctness when receiving the event notification. In addition, two-phase transaction on blockchain is introduced to send warning messages in appropriate regions and time periods. The simulation results show that the proposed mechanism can effectively feedback the correctness of traffic events and provide traceable events with trust verification.

Healthchain: A Blockchain-Based Privacy Preserving Scheme for Large-Scale Health Data

Article

Jun 2019

With the dramatically increasing deployment of the Internet of Things (IoT), remote monitoring of health data to achieve intelligent healthcare has received great attention recently. However, due to the limited computing power and storage capacity of IoT devices, users’ health data are generally stored in a centralized third party, such as the hospital database or cloud, and make users lose control of their health data, which can easily result in privacy leakage and single-point bottleneck. In this paper, we propose Healthchain, a large-scale health data privacy preserving scheme based on blockchain technology, where health data are encrypted to conduct fine-grained access control. Specifically, users can effectively revoke or add authorized doctors by leveraging user transactions for key management. Furthermore, by introducing Healthchain, both IoT data and doctor diagnosis cannot be deleted or tampered with so as to avoid medical disputes. Security analysis and experimental results show that the proposed Healthchain is applicable for smart healthcare system.

SCTSC: A Semicentralized Traffic Signal Control Mode with Attribute-Based Blockchain in IoVs

Article

Apr 2019

Assisting traffic control is one of the most important applications on the Internet of Vehicles (IoVs). Traffic information provided by vehicles is desired since drivers or vehicle sensors are sensitive in perceiving or detecting nuances on roads. However, the availability and privacy preservation of this information are critical while conflicted with each other in the vehicular communication. In this paper, we propose a semicentralized mode with attribute-based blockchain in IoVs to balance the tradeoff between the availability and the privacy preservation. In this mode, a method of control-by-vehicles is used to control signals of traffic lights to increase traffic efficiency. Users are grouped their attributes such as locations and directions before starting the communication. The users reach an agreement on determining a temporary signal timing by interacting with each other without leaking privacy. Final decisions are verifiable to all users, even if they have no a priori agreement and processes of consensus. The mode not only achieves the aim of privacy preservation but also supports responsibility investigation for historical agreements via ciphertext-policy attribute-based encryption (CP-ABE) and blockchain technology. Extensive experimental results demonstrated that our mode is efficient and practical.

Blockchain based secure data sharing system for Internet of vehicles: A position paper

Article

Mar 2019

One of the benefits of Internet of Vehicles (IoV) is improved traffic safety and efficiency, for example due to the capability to share vehicular messages in real-time. While most of the vehicular messages only need to be shared by nearby vehicles, some messages (e.g., announcement messages) may need to be more broadly distributed, for example to vehicles in a wider region. Finding a single trusted entity to store and distribute such messages can be challenging, and vehicles may not be inclined to participate (e.g., generation and distribution of announcement messages) unless they can benefit from such participation. In addition, achieving both security and privacy can be challenging. In this paper, we propose a blockchain based secure data sharing system to address the above challenges in an IoV setting. Specifically, in our system, announcement messages are stored using blockchain. To encourage/incentivize participation, vehicles that faithfully broadcast the announcement messages and/or contribute to the block generation will be rewarded by some cryptocurrency. Our system is also designed to be privacy-preserving and realizes both priori and posteriori countermeasures.

Extended File Hierarchy Access Control Scheme with Attribute Based Encryption in Cloud Computing

Article

Mar 2019

In cloud computing, attribute based encryption (ABE) is often used to solve the challenging issue in secure data storage. In order to lighten the burden of authority center, hierarchical ABE schemes is a very effective way. File hierarchy attribute based encryption (FH-CP-ABE) scheme is presented, which both saves storage space of ciphertext and reduces the computation overhead of encryption. However, it's impossible to encrypt multiple files on the same access level in existing FH-CP-ABE scheme. The scheme is obviously not practical. In this paper, an efficient extended file hierarchy CP-ABE scheme (EFH-CP-ABE) is proposed, which can encrypt multiple files on the same access level. Our scheme is very practical especially for those big institutions or companies which have many hierarchical sectors, since it greatly saves storage space and computation cost for them on the cloud servers. Furthermore, our solution also achieves secure and flexible access control for users in cloud storage. We formally prove the security for our new scheme under the standard model. Finally, we implement the corresponding experiment for EFH-CP-ABE scheme and achieve desirable experimental results.

Revocable Identity-Based Broadcast Proxy Re-Encryption for Data Sharing in Clouds

Article

Feb 2019

Cloud computing has become prevalent due to its nature of massive storage and vast computing capabilities. Ensuring a secure data sharing is critical to cloud applications. Recently, a number of identity-based broadcast proxy re-encryption (IB-BPRE) schemes have been proposed to resolve the problem. However, the IB-BPRE requires a cloud user (Alice) who wants to share data with a bunch of other users (e.g. colleagues) to participate the group shared key renewal process because Alice's private key is a prerequisite for shared key generation. This, however, does not leverage the benefit of cloud computing and causes the inconvenience for cloud users. Therefore, a novel security notion named revocable identity-based broadcast proxy re-encryption (RIB-BPRE) is presented to address the issue of key revocation in this work. In a RIB-BPRE scheme, a proxy can revoke a set of delegates, designated by the delegator, from the re-encryption key. The performance evaluation reveals that the proposed scheme is efficient and practical.

Secure Data Sharing and Customized Services for Intelligent Transportation Based on a Consortium Blockchain

Abstract and Figures

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