Dummy-Trajectory Synthesis: A Privacy-Preserving Approach for Semantic Trajectory Data in IoT-Based LBSN

Trajectory data analysis is crucial in various applications but presents significant privacy risks, as location data can reveal sensitive information. Existing privacy protection methods, such as spatiotemporal K-anonymity and L-diversity, are vulnerable to semantic inference attacks, where public data is exploited to re-identify users. To address these challenges, we propose dummy-trajectory synthesis (DTS), an efficient privacy protection scheme for location-based social networks (LBSNs). DTS enhances privacy by leveraging users’ frequent behavioral sequences to generate synthetic dummy trajectories. Unlike traditional approaches, DTS considers both geographic and semantic data by segmenting historical trajectories into time periods using the OPTICS clustering algorithm. This enables the identification of regions with specific semantic attributes and the mining of semantic trajectory sequences. DTS optimizes dummy trajectory generation by combining Euclidean distances and semantic similarity, ranking historical points and establishing transition relationships. Experimental results show that DTS significantly improves privacy protection and performance compared to existing methods, without compromising service quality. DTS offers a robust solution for protecting trajectory data privacy in LBSNs against transition probability attack for joint time periods.