Real-location Reporting Based Differential Privacy Trajectory Protection for Mobile Crowdsensing

Abstract

Mobile Crowdsensing (MCS) has become an effective technology for urban data sensing and acquisition. But this also brings the risk of trajectory privacy disclosure for participants. Most of the existing efforts attempt to add noise into the reported location information to achieve the trajectory privacy protection of the participating users. However, in many scenarios, the participants are required to report the real-location information (e.g., high-quality map generation, traffic flow monitoring, etc.). To address this problem, we propose a differential privacy based trajectory privacy protection scheme with real-location reporting in MCS. First, we present the definition of trajectory privacy protection based on real path reporting under differential privacy. Second, we give a differential trajectory privacy protection framework to achieve participants trajectory privacy protection under Bayesian inference attacks. Finally, we analyze and prove that differential trajectory privacy problem is an NP-Hard problem. Meanwhile, we also design an approximate algorithm to report participants road segment with the trajectory privacy guarantee. The experimental results on both the simulated data set and the real data set show that our proposed participant's trajectory privacy protection scheme has a good performance.