ZK-V2XChain: A Zero-Knowledge Proof-Enabled Location Privacy Scheme for Sparse IoV Environments

Abstract

Location privacy insparseInternet of Vehicles is difficult to ensure due to limited anonymity, predictable mobility, and prolonged tracking windows. Existing silent-period and pseudonym-based schemes generally assume dense traffic and thus degrade under low-density conditions. This work proposes ZK-V2XChain, a lightweight privacy-preserving framework that integrates a Random Silent Period (RSP) mechanism with blockchain-based Identity Token (IT) authentication and Zero-Knowledge Proof (ZKP) validation. The framework explicitly models sparse-network behavior and enables adaptive, verifiable privacy without compromising efficiency. We design a privacy-preserving IT issuance process using simulated smart contracts and implement decentralized IT verification through RSU–blockchain interaction. Using SUMO mobility traces, ns-3.45 simulations, and MATLAB-based privacy analytics, results show that ZK-V2XChain achieves higher entropy than SAP, RFPM, GLS, and CPS, and approaches the performance of OBS. The maximum anonymity-set size reaches 10.88, and communication overhead remains low (940 bytes). Ablation studies highlight the complementary roles of RSP, ZKP, and blockchain in balancing uncertainty, responsiveness, and issuance stability.