Dummy-Based Schemes for Protecting Movement Trajectories

Abstract

Dummy-based anonymization techniques for protecting the location privacy of mobile users have appeared in the literature. By generating dummies that move in human-like trajectories, this approach shows that the location privacy of mobile users can be preserved. However, the trajectories of mobile users can still be exposed by monitoring the long-term movement patterns of users. We argue that, once the trajectory of a user is identified, the locations of the user are exposed. Thus, it is critical to protect the movement trajectories of mobile users in order to preserve user location privacy. We propose two schemes that generate consistent movement patterns in the long run. Guided by three parameters in a user specified privacy profile, namely, short-term disclosure, long-term disclosure and distance deviation, the proposed schemes derive movement trajectories for dummies. Moreover, since a user may have multiple frequent movement trajectories, we further develop one scheme for users with multiple frequent movement trajectories. The experimental results show that our proposed schemes are more effective than existing works in protecting movement trajectories.