DDLP: Dynamic location data publishing with differential privacy in mobile crowdsensing

Mobile crowdsensing (MCS) has become an effective paradigm to facilitate urban sensing. However, mobile users participating in sensing tasks will face the risk of location privacy leakage when uploading their actual sensing location data. In the application of mobile crowdsensing, most location privacy protection studies do not consider the temporal correlations between locations, so they are vulnerable to various inference attacks, and there is the problem of low data availability. In order to solve the above problems, this paper proposes a dynamic differential location privacy data publishing framework (DDLP) that protects privacy while publishing locations continuously. Firstly, the corresponding Markov transition matrices are established according to different times of historical trajectories, and then the protection location set is generated based on the current location at each timestamp. Moreover, using the exponential mechanism in differential privacy perturbs the true location by designing the utility function. Finally, experiments on the real-world trajectory dataset show that our method not only provides strong privacy guarantees, but also outperforms existing methods in terms of data availability and computational efficiency.