MinHash Hierarchy for Privacy Preserving Trajectory Sensing and Query - Ding, Jiaxin; Ni, Chien-chun; Zhou, Mengyu; Gao, Jie (The 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN '17), 2017)
In this work, we study privacy preserving trajectory sensing and query when mobile entities (e.g., mobile devices or vehicles) move in an environment of checkpoints (e.g, WiFi or cellular towers). The checkpoints detect the appearances of mobile entities in the proximity, meanwhile, employ the MinHash signatures to record the set of mobile entities passing by. We build on the checkpoints a distributed data structure named the MinHash hierarchy, with which one can efficiently answer queries regarding popular paths and other traffic patterns. The MinHash hierarchy has a total of near linear storage, linear construction cost, and logarithmic update cost. The cost of a popular path query is logarithmic in the number of checkpoints. Further, the MinHash signature provides privacy protection using a model inspired by the differential privacy model. We evaluated our algorithm using a large mobility data set and compared with previous works to demonstrate its utilities and performances.