Privacy-Preserving and Collusion-Resistant Data Query Scheme for Vehicular Platoons

Abstract

Data queries play a crucial role in the vehicular platoon, enabling vehicles to obtain traffic information about surrounding road conditions and personalized entertainment information services. However, data query requests from vehicles may expose the vehicle owner’s personal attributes and habits. Although several schemes can address these issues, they are incapable of countering collusion attacks between the coordinating vehicle and roadside units (RSUs). To solve this problem, in this paper we propose a privacy-preserving and collusion-resistant data query scheme, named PCDQ. Specifically, PCDQ uses the Paillier encryption and Chinese Remainder Theorem to protect the query privacy of vehicle owners, allowing the RSU to recover individual data query requests without associating them with the original vehicles. Next, the parameter update mechanism in PCDQ prevents the coordinating vehicle from obtaining the corresponding mapping information between vehicles and query parameters, thereby resisting collusion attacks between the coordinating vehicle and RSUs. In addition, identity-based signcryption is used to ensure secure parameter distribution among vehicles, and the batch verification enables efficient authentication of query requests. Detailed security proofs and analysis demonstrate that PCDQ satisfies multiple security properties, including resistance to collusion attacks and replay attacks, unlinkability, confidentiality, and authentication and data integrity. Experimental results show that, compared to existing solutions, PCDQ performs better in terms of computation overhead, communication overhead, and network performance.