Uncertain trajectory privacy-preserving method of moving object - Shuang WANG; Fu-cai ZHOU; Li-na WU (Tongxin xuebao)
With the development of location based service(LBS) and location-aware devices，the amount of trajectories of moving objects collected by service providers was continuously increasing,meanwhile,it can cause great threaten for personal privacy.Most researches of trajectory privacy preserving were on deterministic data,however,trajectory’s uncertainty was inherent due to the inaccuracy of data acquisition equipment,delayed update,and so on.A new method was prosed to protect the privacy of trajectory data in publishing.It is the first time to present the idea that transforming the trajectory to an uncertain area to cluster.First,a probability statistics method to model the trajectory to an uncertain area was proposed.Second,the similar uncertain area into a cluster was put and sanitized in an equivalence class.Finally,the performance of the proposal was compared with (K δ)-anonymity model in real datasets.