Efficient Anonymous Authentication and Privacy-Preserving Reliability Evaluation for Mobile Crowdsensing in Vehicular Networks
Yudan Cheng, Jianfeng Ma, Zhiquan Liu, Libo Wang, Zuobin Ying, Xin Chen
Abstract
Mobile crowdsensing (MCS) is widely applied in vehicular networks where several sensing vehicles complete the same sensing task. Recently, the privacy and reliability of sensing vehicles have aroused extensive attention of researchers in academia. Although the majority of existing schemes have achieved anonymous authentication with large computation and communication overheads, they do not take the reliability of sensing vehicles into account when selecting sensing vehicles. In this article, we propose an efficient anonymous authentication and privacy-preserving reliability evaluation (AARE) scheme for MCS, which not only improves the efficiency of mutual authentication but also guarantees the reliability of sensing vehicles. Specifically, an efficient anonymous authentication method is proposed to achieve the anonymous authentication of sensing vehicles. Besides, even if a sensing vehicle passes anonymous authentication, it is difficult to fully guarantee its reliability. Then, a privacy-preserving reliability evaluation algorithm is adopted to evaluate the reliability of sensing vehicles. Meanwhile, in dynamic vehicular networks, the reliability of sensing vehicles is uncertain since there exist many attacks, thus, an accurate reputation update algorithm is designed. Subsequently, the privacy features, computation, and communication overheads in the proposed scheme are analyzed. Comparisons with the existing schemes show that the authentication efficiency of the proposed scheme is increased by 46%–74%, the communication overhead is reduced by 50%–80%, and the accuracy of reputation values of sensing vehicles which submit the reliable/unreliable sensing data is improved/reduced by 16%–22%/67%–74%.