A cost sharing mechanism for location privacy preservation in big trajectory data

With increasing development of location-based services (LBSs), location privacy preservation has been one of the most concerned problems. A common method is to let a user generate dummy trajectories, which ensures the location privacy of a lot of users in a small area. However, due to the high cost of generating dummy trajectories, it is not reasonable for only one user to undertake the cost. In this paper, we study the cost sharing problem to determine which user to generate dummy trajectories and receive the payment from the others. We construct an auction based model, where each LBS user as a bidder, reports his privacy cost and dummy trajectories. We propose a cost sharing mechanism, which incentives users to report their true cost and the effective degree of privacy for all the users. We also demonstrate that our mechanism satisfies both incentive compatibility and budget balance. We evaluate the performance of the proposed mechanism via simulated experiments.