PPPV: Privacy-Preserving Position Verification for Internet of Vehicles Monitoring Against Malicious Attacks

Abstract

With the rapid development of the Internet of Vehicles (IoV), achieving trustworthy vehicle position verification while preserving location privacy has become a key requirement in intelligent traffic supervision scenarios such as defense control zones and urban restricted-access areas. Existing privacy-preserving schemes have difficulty simultaneously supporting accurate determination of complex-shaped prohibited areas and efficient computation, and still face malicious attacks such as interference with verification procedures, tampering with communication processes, and privacy inference when determining the positional relationship between vehicles and prohibited areas. To address these issues, this paper proposes an efficient privacy-preserving position verification (PPPV) scheme based on secure multi-party computation (MPC). The scheme supports arbitrary polygonal prohibited areas, including convex, concave, and self-intersecting polygons, thereby improving its applicability in complex IoV supervision scenarios. Based on an improved cross-product determination method, this paper constructs an efficient PPPV protocol under the semi-honest model, achieving near-plaintext computational efficiency while protecting the privacy of both vehicle locations and area boundaries. To resist malicious attacks, this paper further combines Paillier homomorphic encryption, the cut-and-choose method, and zero-knowledge proof to construct a secure PPPV protocol under the malicious model, which can effectively prevent protocol deviations, result tampering, and inference attacks. This paper also conducts formal security proof based on the real/ideal model paradigm, and evaluates the performance of the scheme through benchmark experiments and attack experiments. Experimental results show that the scheme achieves a good balance among efficiency, applicability, and security, providing a deployable trustworthy position verification mechanism for next-generation IoV intelligent supervision applications.