Real-Time Trajectory Data Publishing Method with Differential Privacy

Abstract

With the increasing popularity of location technologies and location-based service applications, a large number of user's trajectory data have been collected. Publishing the real-time statistics data of trajectory streams can be useful in many fields such as intelligent transportation system, urban road planning and road congestion detection. As the trajectory data itself contains a wealth of user's privacy information, the privacy leakage problem has aggravated the risk of data publishing. In order to realize the personalized and uniform privacy preserving of user's trajectory data, the differential privacy model based on data perturbation is introduced, and a privacy preserving algorithm is proposed. The algorithm contains three modules of dynamic privacy budget allocation, privacy approximation and privacy publishing. In the experiment, the performances of the proposed method are verified by using real-life datasets.