Abstrait

TRPF: A Trajectory Private- Preserving Frame Work for Participatory Sensing

Mejil Lieoncy.S, Muthusankar.B,Anitha.A

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. 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’ and the relevant personal privacy. To propose a trajectory privacypreserving framework, named TrPF, for participatory sensing. Based on the framework, improve the theoretical mix-zones model with considering the time factor from the perspective of graph theory. It analyze the threat models with different background knowledge and evaluate the effectiveness of proposal on the basis of information entropy, and then compare the performance of proposal with previous trajectory privacy protections. Finally, the results prove that the proposal can protect participators’ trajectories privacy effectively with lower information loss and costs than what is afforded by the other proposals.