TCPP: Achieving Privacy-Preserving Trajectory Correlation With Differential Privacy

The prevalence of mobile Internet, smart terminal devices, and GPS positioning technology has generated a vast number of trajectory data that location-based applications can utilize. However, delivering LBSs based on trajectories without extra protection may expose the personal information of users and even their social ties. Despite the fact that many works have been offered to achieve differential privacy for trajectory correlation, the vast majority of them only consider the trajectory correlation of a single user, and privacy protection for trajectory correlation amongst multiple users is not considered. Directly applying these works to protect correlation amongst multiple users may lead to the low availability of published trajectory data. To address the above challenges, we propose a trajectory correlation privacy-preserving mechanism (TCPP) that fulfills differential privacy. Specifically, we first apply the Euclidean distance to filter out a set of trajectories whose correlation needs to be protected. Then, we employ the Kalman filter to generate a dataset with high availability from the set of trajectories. Finally, we present a mechanism for publishing trajectories that preserves the trajectory correlation based on a customized privacy budget allocation strategy. Rigid security analysis shows that our proposed mechanism can well preserve the correlation privacy of trajectories. Experimental results on real-world datasets further demonstrate the privacy, availability and time efficiency advantages of our mechanism.

Automated summary

The prevalence of mobile Internet, smart terminal devices, and GPS positioning technology has led to a large amount of trajectory data that location-based applications can use. However, without extra protection, delivering location-based services (LBSs) based on trajectories may expose user's personal information and social ties. Existing works on differential privacy for trajectory correlation focus on single user trajectory correlation and do not consider privacy protection for correlation among multiple users. To address these challenges, we propose a trajectory correlation privacy-preserving mechanism (TCPP) that fulfills differential privacy. Our mechanism filters out correlated trajectories using Euclidean distance, applies Kalman filter for high availability dataset generation, and uses a customized privacy budget allocation strategy for preserving trajectory correlation when publishing trajectories. Rigid security analysis and experimental results on real-world datasets show the effectiveness and advantages of our proposed mechanism in preserving trajectory correlation privacy.