Novel trajectory data publishing method under differential privacy

The existing location-based services have collected a large amount of user trajectory data, and if these data are directly released without any processing, the user's personal privacy will be leaked. At present, differential privacy protection technology is favored by many scholars, but how to apply it reasonably to location-based services is also a challenge for us. Trajectory is spatiotemporal continuous, but most existing methods only consider the single location of moving objects at a certain time without considering the entire trajectory, which may destroy the spatiotemporal integrity of the trajectory. In this paper, we address this problem and firstly propose a Sequence R (SR)-tree structure that satisfies the differential privacy based on the R-tree, and we construct the SR-Tree by using the trajectory sequence instead of the minimum bounding rectangle of the R-tree. Then we put forward an attack model called non-location sensitive information attack, in order to resist this attack, we add noise into the location data and non-location sensitive data using differential privacy techniques. Finally, the Algorithm can be consistently dealt with the problem of data inconsistency after adding noise. Experimental results show that our Algorithm not only has high data availability, operational efficiency, but also has good scalability.