Protecting Locations with Differential Privacy against Location-Dependent Attacks in Continuous LBS Queries

With the development of location-based services (LBS), concerns on location privacy frequently arise. Location data often contains users' sensitive information, and direct release it may pose a threat to users' privacy. Differential privacy (DP), as a privacy preserving method with solid mathematical foundation, has been widely used in location data release. However, most if not all of the existing location DP mechanisms only consider static scenarios or perturb the location at single timestamp, which are vulnerable to the so-called location-dependent attacks (LDA) in continuous LBS queries. In this paper, an optimal location DP mechanism against LDA is proposed. Firstly, the necessary conditions for LDA defense are derived by combining the perturbation mechanism of location DP. Then the algorithm of safe perturbance region generation is established to dynamically calculate the perturbation range at each timestamp. Finally, we set up the optimization problem with the real-time quality loss as the optimization objective and the location DP and safe perturbance region as the optimization conditions, and realize the optimal DP mechanism for LDA by solving the optimization problem. Experiment results on real-world datasets show that our mechanism can effectively resist LDA, which also balance privacy protection and data utility well.