Efficient Privacy-Preserving Authentication Scheme With Fine-Grained Error Location for Cloud-Based VANET

Abstract

As an emerging technology, cloud-based Vehicular Ad-Hoc Network (VANET) can significantly improve the efficiency of transportation systems by alleviating traffic congestion and optimizing resource management. However, since the information transmitted in VANET is distributed in an open-access environment, security and privacy are now becoming vital concerns, especially when attackers own more and more resources. Recently, many tamper-proof devices (TPD)-based authentication schemes have been proposed to tackle the above-mentioned challenges, while most of them are dependent on ideal TPDs with very strong security assumptions. Moreover, when the aggregate signature verification fails, these schemes can do nothing but discard the aggregate signature completely, which makes them impractical in VANET. To address the above concerns, we propose a more realistic TPD-based authentication scheme with privacy-preserving features for cloud-based VANET. Specifically, the off-line self-updating method is utilized to update the data in TPD periodically to resist side-channel attacks. Furthermore, an efficient fine-grained error location algorithm is designed to quickly detect all invalid signatures within a problematic aggregate signature scenario. Our performance analysis shows that the proposed scheme outperforms the existing ones in terms of security, computation delay and communication overhead, thus more suitable in practical cloud-based VANET environment.