RATR: Optimized Trajectory Release With Temporal Local Differential Privacy

Time series data has a wide range of real-life applications, including financial forecasting, health monitoring, and social media analysis. However, this data often contains substantial individual privacy, and directly releasing them may lead to privacy infringement. Although existing research has proposed methods based on centralized differential privacy or local differential privacy to protect the privacy of time series data by injecting noise into the data, these methods may not be feasible in value-critical applications. Our previous work has introduced the TLDP model, which perturbs temporal data to ensure privacy guarantee while retaining original values. However, challenges persist when applying these methods to data exhibiting spatiotemporal correlations, such as trajectories. This paper proposes a novel trajectory release framework, Region-Aware Trajectory Re-sorting (RATR), which builds upon the temporal perturbation mechanism of TLDP by further introducing region-aware reachable constraints based on real road networks. In this way, it ensures privacy while significantly improving the spatial data utility. We construct region-aware reachable constraints and design a re-sorting algorithm to optimize the perturbed trajectories. Extensive experiments conducted on three real-world datasets demonstrate that our method significantly outperforms existing state-of-the-art methods in terms of data utility while maintaining a lower overall cost.