Differentially private counting of users’ spatial regions

Abstract

Mining of spatial data is an enabling technology for mobile services, Internet-connected cars and the Internet of Things. But the very distinctiveness of spatial data that drives utility can cost user privacy. Past work has focused upon points and trajectories for differentially private release. In this work, we continue the tradition of privacy-preserving spatial analytics, focusing not on point or path data, but on planar spatial regions. Such data represent the area of a user’s most frequent visitation—such as “around home and nearby shops”. Specifically we consider the differentially private release of data structures that support range queries for counting users’ spatial regions. Counti..