1 Introduction Trajectory data serves as a cornerstone for numerous real-world applications,ranging from smart transportation systems to urban logistics.The rich sources of trajectory data offer profound insights into...1 Introduction Trajectory data serves as a cornerstone for numerous real-world applications,ranging from smart transportation systems to urban logistics.The rich sources of trajectory data offer profound insights into mining movement patterns and enable intelligent decisionmaking for large-scale users.Yet,trajectories also carry inherent privacy risks[1].The spatiotemporal nature of trajectory data can inadvertently expose sensitive personal information,such as the data owner’s home or work locations and travel routines,thereby raising significant concerns about personal privacy[2].展开更多
基金partially supported by the National Key Research and Development Program of China(Grant No.2023YFF0725103)the National Natural Science Foundation of China(NSFC)(Grant Nos.62425202,U21A20516,62336003)+2 种基金the Beijing Natural Science Foundation(Z230001)the Fundamental Research Funds for the Central Universities(No.JK2024-03)the Didi Collaborative Research Program and the State Key Laboratory of Complex&Critical Software Environment(SKLCCSE).
文摘1 Introduction Trajectory data serves as a cornerstone for numerous real-world applications,ranging from smart transportation systems to urban logistics.The rich sources of trajectory data offer profound insights into mining movement patterns and enable intelligent decisionmaking for large-scale users.Yet,trajectories also carry inherent privacy risks[1].The spatiotemporal nature of trajectory data can inadvertently expose sensitive personal information,such as the data owner’s home or work locations and travel routines,thereby raising significant concerns about personal privacy[2].
