CALRA: Practical Conditional Anonymous and Leakage-Resilient Authentication Scheme for Vehicular Crowdsensing Communication

Abstract

Vehicular crowdsensing (VCS) has aroused extensive attention because of its ability to provide comprehensive data services for intelligent transportation systems. Wherein, secure data transmission is a prerequisite for realizing the above benefits of VCS. Unfortunately, although many works on secure data sharing have been proposed, these schemes suffer from practical weaknesses such as data source authentication, malicious identity traceability, and inefficiency. In this paper, we propose a practical conditional anonymization and leakage-resilient authentication solution for vehicular crowdsensing communication (CALRA). Our proposal not only resists the leakage of sensitive information about vehicles but also realizes the authentication of data senders, while guaranteeing the integrity, authenticity, and confidentiality of data. Besides, CALRA exploits traceability technology to pursue malicious/illegal participants, thus avoiding participants’ accountability evasion caused by absolute anonymity. Furthermore, our CALRA solution delegates complex computational processes into an offline formulation to reduce computational and communication overheads. Finally, our scheme is proved to be secure and unforgeable through the random oracle model, and the performance evaluation illustrates that our CALRA proposal is superior and practical.