Location privacy is critical in vehicular networks, where drivers' trajectories and personal information can be exposed, allowing adversaries to launch data and physical attacks that threaten drivers' safety and personal security. This survey reviews comprehensively different localization techniques, including widely used ones like sensing infrastructure-based, optical vision-based, and cellular radio-based localization, and identifies inadequately addressed location privacy concerns. We classify Location Privacy Preserving Mechanisms (LPPMs) into user-side, server-side, and user-server-interface-based, and evaluate their effectiveness. Our analysis shows that the user-server-interface-based LPPMs have received insufficient attention in the literature, despite their paramount importance in vehicular networks. Further, we examine methods for balancing data utility and privacy protection for existing LPPMs in vehicular networks and highlight emerging challenges from future upper-layer location privacy attacks, wireless technologies, and network convergences. By providing insights into the relationship between localization techniques and location privacy, and evaluating the effectiveness of different LPPMs, this survey can help inform the development of future LPPMs in vehicular networks.

翻译：位置隐私在车联网中至关重要，因为驾驶员的轨迹和个人信息可能暴露，使得攻击者能够发动数据和物理攻击，威胁驾驶员的安全与人身保障。本综述全面考察了不同的定位技术，包括广泛使用的基于传感基础设施、光学视觉和蜂窝无线电的定位方法，并指出了尚未充分解决的位置隐私问题。我们将位置隐私保护机制（LPPMs）分为用户侧、服务器侧和用户-服务器接口侧，并评估其有效性。分析表明，尽管用户-服务器接口侧的LPPMs在车联网中具有极其重要的地位，但在文献中受到的关注仍显不足。此外，我们探讨了车联网中现有LPPMs在数据效用与隐私保护之间平衡的方法，并强调了来自未来上层位置隐私攻击、无线技术和网络融合的新兴挑战。通过深入分析定位技术与位置隐私之间的关系，并评估不同LPPMs的有效性，本综述可为车联网中未来LPPMs的发展提供参考。