In the era of Internet of Things, huge volume of spatio-temporal data is generated and collected 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 will present geometric algorithms to sense trajectories, mine mobility patterns, and protect individuals privacy We employ distributed checkpoints (roadside units, WiFi access points, cellular towers, etc ) to record appearances of mobile entities (vehicles, pedestrians, etc ) The checkpoints and mobile entities can communicate and compute to collect data and discover patterns In this network setting, we study two cases The first one is trajectory topological clustering Checkpoints form a triangular mesh in the domain to detect the topology of mobile entity trajectories We develop distributed computation methods for harmonic one-form using Hodge decomposition，such that the topology of a trajectory can be obtained by integrating harmonic one-form weights of edges visited The second case is popular path pattern mining We apply a hierarchical data structure based on a succinct differential private MinHash signature to mine frequent traffic patterns efficiently

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.