TrPF: A Trajectory Privacy-Preserving Framework for Participatory Sensing

The ubiquity of the various cheap embedded sensors on mobile devices, for example cameras, microphones, accelerometers, and so on, is enabling the emergence of participatory sensing applications. While participatory sensing can benefit the individuals and communities greatly, the collection and analysis of the participators´ location and trajectory data may jeopardize their privacy. However, the existing proposals mostly focus on participators´ location privacy, and few are done on participators´ trajectory privacy. The effective analysis on trajectories that contain spatial-temporal history information will reveal participators´ whereabouts and the relevant personal privacy. In this paper, we propose a trajectory privacy-preserving framework, named TrPF, for participatory sensing. Based on the framework, we improve the theoretical mix-zones model with considering the time factor from the perspective of graph theory. Finally, we analyze the threat models with different background knowledge and evaluate the effectiveness of our proposal on the basis of information entropy, and then compare the performance of our proposal with previous trajectory privacy protections. The analysis and simulation results prove that our proposal can protect participators´ trajectories privacy effectively with lower information loss and costs than what is afforded by the other proposals.