Enabling Private Cooperative Sensing Sharing in Vehicular Networks via Encrypted Spatial Matching

Xinrui Zhang, Pincan Zhao, Rongxing Lu, Jason Jaskolka, Suprio Ray

Abstract: Connected and Autonomous Vehicles (CAVs) equipped with diverse sensors can enhance environmental perception through cooperative sensing, overcoming individual sensor limitations such as restricted range and occlusion. However, privacy concerns regarding location exposure and data leakage significantly hinder widespread adoption. This paper presents a comprehensive privacy-preserving cooperative sensing framework that enables secure data sharing among CAVs without compromising performance. We introduce two key innovations: the Vehicular Spatial Index Tree (VSITree), which provides efficient spatial indexing while preventing location leakage through cryptographic encoding, and the Vehicular Attribute Matching Protocol (VAMP), which enables oblivious membership testing between encrypted sensing data and queries. Our framework leverages arithmetic secret sharing and predicate encryption to protect both sensing providers and requesters throughout the data lifecycle. The system is designed to operate through roadside units (RSUs) that facilitate secure matching and aggregation without learning sensitive information. Theoretical analysis and extensive simulations demonstrate the security and efficiency properties of our approach, confirming its resilience against various attack vectors while maintaining real-time performance suitable for safety-critical vehicular applications.

External IDs:doi:10.1109/jiot.2025.3650147