DP-FETC: a differentially private trajectory publishing method based on feature extraction and trajectory correlation

With the widespread application of location-based services, how to effectively publish trajectories while preserving users' privacy has become a critical challenge. Existing privacy-preserving trajectory publishing methods often face issues such as missing in synthetic trajectories, low data utility, and privacy leakage of users' social relationships due to trajectory correlation. To address these issues, a differentially private trajectory publishing method based on feature extraction and trajectory correlation (DP-FETC) is proposed in this paper. The method is composed of two synergistic core algorithms. First, a trajectory synthesis algorithm (SynFE) employs adaptive grid discretization to extract key statistical features—including weighted location, origin-destination (OD), and length distributions—to generate high-fidelity synthetic trajectories that maintain high data utility. Second, to mitigate the risk of social relationship disclosure, an innovative correlated trajectory protection algorithm (PreOD) identifies highly correlated trajectories using a novel metric based on stay duration at points of interest. It then applies a targeted perturbation exclusively to the OD points of these high-risk trajectories. This strategy effectively obscures social links while minimizing the impact on overall data quality. Experimental results on two real-world datasets validate that the proposed method has good data utility while providing robust privacy guarantees.