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A Privacy-Preservation Framework based on Biometrics Blockchain (BBC) to Prevent Attacks in VANET

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A Privacy-Preservation Framework based on Biometrics Blockchain (BBC) to Prevent Attacks in VANET. / Alharthi, Abdullah; Ni, Qiang; Jiang, Richard.
In: IEEE Access, Vol. 9, 04.06.2021, p. 87299-87309.

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@article{9eb050c2881642ed9c8dd796211c12aa,
title = "A Privacy-Preservation Framework based on Biometrics Blockchain (BBC) to Prevent Attacks in VANET",
abstract = "In the near future, intelligent vehicles will be part of the Internet of Things (IoT) and will offer valuable services and opportunities that could revolutionise human life in smart cities. The Vehicular Ad-hoc Network (VANET) is the core structure of intelligent vehicles. It ensures the accuracy and security of communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) modes to enhance road safety and decrease traffic congestion. However, VANET is subject to security vulnerabilities such as denial-of-service (DoS), replay attacks and Sybil attacks that may undermine the security and privacy of the network. Such issues may lead to the transmission of incorrect information from a malicious node to other nodes in the network. In this paper, we present a biometrics blockchain (BBC) framework to secure data sharing among vehicles in VANET and to retain statuary data in a conventional and trusted system. In the proposed framework, we take advantage of biometric information to keep a record of the genuine identity of the message sender, thus preserving privacy. Therefore, the proposed BBC scheme establishes security and trust between vehicles in VANET alongside the capacity to trace identities whenever required. Simulations in OMNeT++, veins and SUMO were carried out to demonstrate the viability of the proposed framework using the urban mobility model. The performance of the framework is evaluated in terms of packet delivery rate, packet loss rate and computational cost. The results show that our novel model is superior to existing approaches.",
keywords = "Vehicle driving, Intelligent vehicles, Vehicular and wireless technologies, VANET, Biometric blockchain, Privacy preservation",
author = "Abdullah Alharthi and Qiang Ni and Richard Jiang",
year = "2021",
month = jun,
day = "4",
doi = "10.1109/ACCESS.2021.3086225",
language = "English",
volume = "9",
pages = "87299--87309",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - A Privacy-Preservation Framework based on Biometrics Blockchain (BBC) to Prevent Attacks in VANET

AU - Alharthi, Abdullah

AU - Ni, Qiang

AU - Jiang, Richard

PY - 2021/6/4

Y1 - 2021/6/4

N2 - In the near future, intelligent vehicles will be part of the Internet of Things (IoT) and will offer valuable services and opportunities that could revolutionise human life in smart cities. The Vehicular Ad-hoc Network (VANET) is the core structure of intelligent vehicles. It ensures the accuracy and security of communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) modes to enhance road safety and decrease traffic congestion. However, VANET is subject to security vulnerabilities such as denial-of-service (DoS), replay attacks and Sybil attacks that may undermine the security and privacy of the network. Such issues may lead to the transmission of incorrect information from a malicious node to other nodes in the network. In this paper, we present a biometrics blockchain (BBC) framework to secure data sharing among vehicles in VANET and to retain statuary data in a conventional and trusted system. In the proposed framework, we take advantage of biometric information to keep a record of the genuine identity of the message sender, thus preserving privacy. Therefore, the proposed BBC scheme establishes security and trust between vehicles in VANET alongside the capacity to trace identities whenever required. Simulations in OMNeT++, veins and SUMO were carried out to demonstrate the viability of the proposed framework using the urban mobility model. The performance of the framework is evaluated in terms of packet delivery rate, packet loss rate and computational cost. The results show that our novel model is superior to existing approaches.

AB - In the near future, intelligent vehicles will be part of the Internet of Things (IoT) and will offer valuable services and opportunities that could revolutionise human life in smart cities. The Vehicular Ad-hoc Network (VANET) is the core structure of intelligent vehicles. It ensures the accuracy and security of communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) modes to enhance road safety and decrease traffic congestion. However, VANET is subject to security vulnerabilities such as denial-of-service (DoS), replay attacks and Sybil attacks that may undermine the security and privacy of the network. Such issues may lead to the transmission of incorrect information from a malicious node to other nodes in the network. In this paper, we present a biometrics blockchain (BBC) framework to secure data sharing among vehicles in VANET and to retain statuary data in a conventional and trusted system. In the proposed framework, we take advantage of biometric information to keep a record of the genuine identity of the message sender, thus preserving privacy. Therefore, the proposed BBC scheme establishes security and trust between vehicles in VANET alongside the capacity to trace identities whenever required. Simulations in OMNeT++, veins and SUMO were carried out to demonstrate the viability of the proposed framework using the urban mobility model. The performance of the framework is evaluated in terms of packet delivery rate, packet loss rate and computational cost. The results show that our novel model is superior to existing approaches.

KW - Vehicle driving

KW - Intelligent vehicles

KW - Vehicular and wireless technologies

KW - VANET

KW - Biometric blockchain

KW - Privacy preservation

U2 - 10.1109/ACCESS.2021.3086225

DO - 10.1109/ACCESS.2021.3086225

M3 - Journal article

VL - 9

SP - 87299

EP - 87309

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -