A Privacy-Preserving Proximity Testing Using Private Set Intersection for Vehicular Ad-Hoc Networks
IEEE Transactions on Industrial Informatics（2022）
摘要
Proximity testing technologies have been of increasing importance in vehicular
ad-hoc
networks (VANETs), especially in location-based services. However, there exist several known challenges in most existing proximity testing methods. For instance, during proximity testing, how to protect the location privacy of users, guarantee the fairness trait of both communication parties, and reduce computational costs is challenging. In this article, we present an efficient privacy-preserving proximity testing scheme using private set intersection (PSI) and differential privacy. In our design, a Chebyshev-based PSI is constructed to achieve location privacy with low energy consumption during the proximity testing process. Furthermore, geo-indistinguishability is employed in our scheme to generate virtual points as inputs set of PSI, which further protects the location privacy from exposure and provides resistance to collusion attacks. Fairness requirement is alsosatisfied in our scheme. The performance evaluation shows that the proposed scheme achieves good efficiency and is suitable for VANETs.
更多查看译文
关键词
Chebyshev chaotic maps,privacy protection,private set intersection (PSI),proximity testing
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要