Identifying Unsafe Routes for Network-Based Trajectory Privacy

In this paper, we propose a privacy model that offers trajectory privacy to the requesters of Location-Based Services (LBSs), by utilizing an underlying network of user movement. The privacy model has been implemented as a framework that (i) reconstructs the user movement from a series of independent location updates, (ii) identifies routes where user privacy is at risk, and (iii) anonymizes online user requests for LBSs to protect the requester for as long as the service withstands completion. In order to achieve (iii), we propose two anonymization techniques, the K–present (weak) and the K–frequent (strong) trajectory anonymity, and a second chance approach that takes over when anonymization fails to ensure that the privacy of the user is preserved. To the best of our knowledge, this is the first work to propose a trajectory privacy model that utilizes an underlying network of user movement to offer in an interactive way personalized privacy to online user requests on trajectory data.