Walk Alone and Be Fast: Trajectory Privacy-preserving in Complicated Environment

Trajectories are location samples ordered by sampling time, which is useful to multiple mobility-related applications. However, publication of these trajectories may cause serious personal privacy leakage. In this paper, we propose an approach called Walk Alone and Be Fast (WABF) to protect trajectory privacy against semantic location attack and maximum moving speed attack. WABF reduces the whole trajectories' exposure probability. At last, we conduct a set of comparative experimental studies on a real-world data set, the results show that WABF is effective and the information loss is much lower than k-anonymity methods.