Huge volume of spatio-temporal data is generated with the advances of sensing techniques and computing capability. It provides us great opportunities to study mobility patterns to enhance our living qualities while it also raises privacy issues when collecting individuals' trajectories. In this presentation, I describe geometric algorithms to sense trajectories, mine mobility patterns, and protect individuals' privacy.
Instead of using location devices equipped by mobile entities (vehicles, pedestrians, etc.), we employ checkpoints (roadside units, WiFi access points, cellular towers, etc.) to record appearances of the mobile entities.
We apply a hierarchical data structure based on a succinct differential private MinHash signature to frequent traffic patterns efficiently.
We also demonstrate an adversary can re-identify individuals through frequent location attack, co-location attacks and motif attacks. We introduce an approach mixing ID during the co-location events. The performance of the approach is effective theoretically and empirically to defeat statistical attacks.
Jiaxin Ding is now a Ph.D. candidate in Computer Science, Stony Brook University. His advisor is Professor Jie Gao. He will defend his dissertation in November. Jiaxin Ding received his B.S. in EECS, and B.A. in Economics, Peking University, in 2012. His research interests include spatio-temporal data ming, differential privacy, computational geometry, and Internet of Things. His papers are published in the conferences of IPSN, INFOCOM, MobiHoc, and SIGSPATIAL.