Dummy-Based User Location Anonymization Under Real-World Constraints

According to the growth of mobile devices equipped with a GPS receiver, a variety of location based services (LBSs) have been launched. Since location information may reveal private information, preserving location privacy has become a significant issue. Previous studies proposed methods to preserve a users' privacy; however, most of them do not take physical constraints into consideration. In this paper, we focus on such constraints and propose a location privacy preservation method that can be applicable to a real environment. In particular, our method anonymizes the user's location by generating dummies which we simulate to behave like real human. It also considers traceability of the user's locations to quickly recover from an accidental reveal of the user's location. We conduct an experiment using five users' real GPS trajectories and compared our method with previous studies. The results show that our method ensures to anonymize the user's location within a pre-determined range. It also avoids fixing the relative positions of the user and dummies, which may give a hint for an LBS provider to identify the real user. In addition, we conducted a user experiment with 22 participants to evaluate the robustness of our method against humans. We asked participants to observe movements of a user and dummies and try to find the real user. As a result, we confirmed that our method can anonymize the users' locations even against human's observation.