A Novel Approach for Trajectory Partition Privacy in Location-Based Services

Location-based service (LBS) devices have enhanced daily life convenience but raised privacy concerns regarding user location data. For this, this research presents a novel approach to safeguard trajectory privacy through Similarity-based Trajectory Partitioning (STSM). The process begins by dividing trajectory equivalence classes based on distinct timestamps. Trajectory similarity is then assessed within each class using metrics such as Frechet distance, trajectory direction, and speed. Subsequently, a trajectory graph is constructed for each class, and the Dijkstra algorithm is applied to partition the graph using the k-subgraph method, effectively converting trajectory partitioning into graph partitioning. To meet publication requirements, Laplace noise is added to sensitive data linked to nodes and edges within the subgraph. Comparative experiments on real-world data confirm the efficacy, rationality, data availability, and privacy protection capabilities of the proposed trajectory partitioning algorithm, underscoring its superiority over alternative methods.