Journal
VEHICULAR COMMUNICATIONS
Volume 41, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.vehcom.2023.100602
Keywords
VANETs; Privacy -preserving authentication; Cross -domain; Group signature; Blockchain
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Vehicular Ad-Hoc Networks (VANETs) have improved driving safety and comfort through vehicular wireless communication technology. However, existing authentication protocols in VANETs have limitations in terms of privacy protection, malicious entity tracking, and cross-domain authentication. To address these challenges, we propose a secure and effective group signature scheme for anonymous authentication and traceable identity within a domain, and a blockchain-based privacy-preserving cross-domain authentication protocol (BCGS) that integrates both blockchain and group signature. Our evaluations show that BCGS outperforms other approaches in terms of security, computation, and storage costs.
Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Secure authentication among vehicles in VANETs is an important requirement, but there are various limitations in many existing authentication protocols, for example, in terms of privacy protection, malicious entity tracking, and cross-domain authentication. In response, we first reconstruct a secure and effective short group signature scheme for realizing anonymous authentication and traceable identity within a domain. We then propose a secure blockchain-based privacy-preserving cross-domain authentication protocol for VANETs, namely BCGS, by integrating both blockchain and group signature. In BCGS, blockchain is leveraged to facilitate trusted information sharing and consequently supports cross-domain authentication among vehicles, and the group signature scheme is used to provide conditional privacy protection. Our comprehensive security and performance evaluations demonstrate that BCGS outperforms other similar approaches in terms of security, computation, and storage costs.(c) 2023 Elsevier Inc. All rights reserved.
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