Location privacy threats and protections in 5G/6G vehicular networks: A comprehensive review

The integration of advanced localization and sensing technologies in 5G/6G vehicular networks (VNs) significantly enhances service intelligence but introduces severe cross-layer privacy threats. As adversaries exploit physical-layer signals and multi-source data fusion, traditional single-layer Location Privacy-Preserving Mechanisms (LPPMs) become increasingly inadequate. This survey systematizes the evolving landscape of location privacy in VNs by mapping specific tracking attacks to the vulnerabilities of existing LPPMs. We categorize current defenses into user-side, server-side, and user-server-interface approaches, critically evaluating their performance constraints in terms of feasibility, latency, overhead, and deployment realism. Our analysis identifies a critical gap in the literature regarding interface-level protections and the practical trade-offs between theoretical privacy guarantees and operational data utility in highly dynamic environments. Finally, we outline emerging vulnerabilities driven by advanced wireless technologies and AI-enabled inference, providing a structured roadmap for developing inference-resilient and cross-layer LPPMs in next-generation VNs.