Location privacy and security in self-driving vehicles: a ZKP-enhanced ECDDH based authentication framework

The rapid evolution of self-driving vehicles (SDVs) has necessitated the development of robust authentication mechanisms to ensure secure and privacy-preserving vehicle communication. Traditional authentication protocols often expose vehicle location information, raising concerns about tracking and unauthorized surveillance. This paper proposes a novel Zero-Knowledge Proof (ZKP)-enhanced Elliptic Curve Decisional Diffie-Hellman (ECDDH) authentication framework that enables SDVs to prove their presence within a geofenced area without revealing their exact location. The proposed protocol leverages 5G-enabled edge computing to optimize computational efficiency and authentication latency while ensuring scalability in high-density vehicular networks. The proposed framework is formally validated using BAN logic, proving its resilience against replay attacks, location spoofing, and unauthorized access. Performance evaluations conducted in MATLAB demonstrate the efficiency of the protocol, with results indicating an authentication latency of approximately 54.7 ms (100 vehicles), a constant communication overhead of 448 bytes per session, and a 100% authentication success rate. Comparative analysis with ECDH and RSA-based authentication schemes highlights the protocol’s superior security guarantees and optimized communication overhead. The findings confirm that the proposed authentication mechanism is an effective solution for ensuring privacy-preserving authentication in autonomous vehicular networks, making it a viable approach for securing future intelligent transportation systems.