AnotherMe: A Location Privacy Protection System Based on Online Virtual Trajectory Generation

Nowadays, location-based services (LBS) are becoming increasingly important and popular. However, many LBSs are probable to collect the location information of users, which leads to the leakage of location privacy. To address this problem, dummy-based schemes have been proposed by researchers. Nevertheless, most of them only consider semantic information instead of points of interest (POIs), so the virtual trajectories may be detected by advanced data mining techniques. Besides, some of them are offline or non-local, which are not suitable for online LBS scenarios. In this paper, we design AnotherMe, an online and local location privacy-preserving system based on virtual trajectory generation, and develop the system on Android and iOS platforms. The AnotherMe system has two main functions. One is to generate virtual users located in different cities by imitating the real user's moving pattern and mapping the real user's POIs, and the other is to generate virtual trajectories that are indistinguishable from real trajectories with the help of Amap API. Therefore, the AnotherMe system can preserve continuous location privacy, and even advanced data mining techniques are difficult to distinguish between the real trajectory and the corresponding virtual trajectory. Due to low response time and battery consumption, the AnotherMe system is practical for location privacy protection. Furthermore, experimental results show that the virtual trajectories generated by our solution are more indistinguishable from real trajectories than similar solutions, and the average recognition rate of virtual trajectories is 53.8%, which is close to random guessing (50%).