车联网轨迹隐私保护研究进展

轨迹隐私保护对车联网（IoV）的发展至关重要，归纳和分析现有研究方法有重要意义。车联网轨迹隐私保护思想有轨迹模糊、假名更换和轨迹加密等3类，实现方法分别有基于用户真实轨迹的方法和基于哑元轨迹的方法、基于混合区域的方法和基于路径混淆的方法、基于私密信息检索（PIR）协议的方法和基于空间转换的方法。首先，介绍和归纳了研究背景和常见攻击等车联网轨迹隐私保护关键问题；然后，从方法思想、科学问题、方法演进等方面详细综述了现有车联网轨迹隐私保护方法，并阐述了需深入研究的难题；在此基础上，总结了代表性方案的隐私保护度、抗攻击性、复杂度等性能指标；最后展望了车联网轨迹隐私保护的未来研究方向。

Research progress in Internet of vehicles trajectory privacy protection

Trajectory privacy protection is critical to the development of Internet of Vehicles (IoV), which makes it important to summarize and analyze existing research methods. Existing IoV trajectory privacy protection methods can be divided into three categories: trajectory obfuscation, pseudonym change and trajectory encryption. Trajectory obfuscation can be achieved based on users' real trajectory or dummy trajectory. Pseudonym change can be achieved based on mix-zone or path confusion. Trajectory encryption can be achieved based on Private Information Retrieval (PIR) protocol or spatial transformation. Firstly, the research background and common attacks were introduced and summarized in this paper. Secondly, existing IoV trajectory privacy protection methods were surveyed from the aspects of methodology, scientific problem and method evolution. The problems need to be further studied also were elaborated. Furthermore, the performances of representative schemes were summarized, such as privacy protection, attack resistance and complexity. Finally, the future research directions of IoV trajectory privacy protection was prospected.