SCTP: Achieving Semantic Correlation Trajectory Privacy-Preserving With Differential Privacy

Abstract

With the rapid proliferation of vehicular technology, location-based services (LBS) have become a crucial component of Internet of Vehicles (IoV) applications, such as map navigation and health tracking. These applications rely on users' location information to provide services, enabling users to effectively share their locations, access information about nearbyactivities, and engage in real-time communication. However, the extensive collection and sharing of location data pose serious challenges to the semantic privacy preservation of user locations. To address these challenges in IoV, we propose a Semantic Correlation Trajectory Privacy-Preserving mechanism (SCTP). The SCTP combines the Hidden Markov Models (HMM) with differential privacy, aiming to protect the semantic privacy of user trajectory locations while maintaining high-quality location services and data usability. Our scheme introduces a trajectory prediction algorithm based on HMM, which dynamically and accurately predicts user trajectories and generates highly available semantically correlated trajectory datasets. Additionally, we design a personalized privacy budget allocation strategy based on semantic frequency. By assigning privacy weights, we significantly improve the usability of trajectory data while protecting data privacy. Theoretical analysis and experimental validation demonstrate that SCTP rigorously adheres to $\varepsilon$-differential privacy standards while exhibiting significant advantages in safeguarding the semantic privacy of user locations.