车联网中基于雾计算和多TA的条件隐私保护认证方案

车联网在生活中扮演着越来越重要的角色，它可以有效地防止交通拥堵从而减少交通事故。然而，在车联网中总是有非法车辆试图接入车联网并发布虚假消息。此外，现有方案多数存在计算效率低下的问题。针对上述存在的问题进行了研究，提出了一种车联网中基于雾计算和多TA的条件隐私保护认证方案。在保护车辆用户身份的条件下实现了车辆、雾节点、TA三者之间的身份认证，且在车辆追踪阶段可以还原车辆用户的真实身份，从而实现条件隐私保护。雾计算的使用降低了方案的计算和通信开销，同时多TA模型的使用也解决了单TA单点故障的问题。安全性证明和性能分析的结果表明该方案是安全且高效的。最后对当前方案进行了总结以及对未来研究作出了展望。

Conditional privacy-preserving authentication scheme based on fog computing and multi TA in VANET

VANET plays an increasingly important role in life. It can effectively prevent traffic congestion and reduce traffic accidents. However, in VANET, there are always illegal vehicles trying to access the VANET and publishing false information. In addition, most of the existing schemes have the problem of low computational efficiency. Aiming at the above problems, the paper proposed the conditional privacy-preserving authentication scheme based on fog computing and multi-TA. Under the condition of protecting the identity of vehicle users, it realized the identity authentication among vehicle, fog node and TA, and the real identity of vehicle users could be restored in the vehicle tracking stage, thus realizing conditional privacy protection. The use of fog computing reduced the calculation and communication overhead of the scheme, and the use of multi TA model also solved the problem of single TA single point fault. The results of security and performance analysis show that the scheme is safe and efficient. Finally, it summarized the current scheme and prospected the future research.