Ph.D. Preliminary Exam - Ashwin Natarajan

Date: 02 Oct, 2009
Time: 2:00 PM
Location: 223 Atanasoff Hall
Topic: Privacy Preserving Large Scale Trajectory Data Publishing
Major Professor(s): Johnny S. Wong and Ying Cai


Abstract:

Location data containing the detailed, time-series movement of mobile users can be of great value to enterprise and urban planning, transportation scheduling and so on. However, making location data publicly accessible is generally prohibited, because a person's whereabouts can imply highly sensitive private information. In our work, we investigate location depersonalisation of a large set of trajectory data from the perspective of location privacy protection so that these data can be published. Since trajectory data contains detailed and long-time movement of users, it is possible that adversaries exist who might possess some background knowledge about the presence or absence of users at certain locations. This partial knowledge about certain locations belonging to the trajectory of a user can help the adversary to track down the remaining locations of the user's trajectory leading to privacy leaks. Moreover, publishing large scale location data can incur significant I/O and CPU costs. In our preliminary work, we identify a set of attacks and try to propose privacy protection algorithms to protect the users from these threats. We also propose a depersonalisation data structure called TC-tree which can reduce the CPU and I/O costs incurred in publishing these trajectories.