ADGTrace: Achieving Adaptive Trajectory Synthesis with Generated Data

User trajectory publication has promoted various location-based applications like user travel recommendation. However, possible privacy leakages have hindered more inclusive trajectory data analysis and utilization. Privacy-preserving trajectory synthesis is a popular approach to address the above privacy issues. Existing methods unavoidably produce low trajectory utility since they usually apply perturbed versions of human moving patterns. Worse still, they cannot adaptively adjust this synthesis according to the varying granularity demands of different users. This paper proposes a novel adaptive trajectory synthesis framework with generated data, namely ADGTrace. Our model achieves privacy preservation without introducing additional noise while maintaining high adaptation. ADGTrace directly synthesizes artificial trajectories that share the similar patterns with real ones through a generative and selective optimization process. Additionally, we present a grid granularity alignment strategy to achieve adaptive trajectory synthesis, satisfying varying user demands. Extensive experiments on real-world datasets demonstrate the superiority of ADGTrace over the state-of-the art methods under various utility metrics, maintaining strong attack resilience.