Measuring Privacy Loss in Distributed Spatio-Temporal Data

arXiv:2402.11526v1 Announce Type: new
Abstract: Statistics about traffic flow and people's movement gathered from multiple geographical locations in a distributed manner are the driving force powering many applications, such as traffic prediction, demand prediction, and restaurant occupancy reports. However, these statistics are often based on sensitive location data of people, and hence privacy has to be preserved while releasing them. The standard way to do this is via differential privacy, which guarantees a form of rigorous, worst-case, person-level privacy. In …

applications arxiv cs.cr data demand distributed driving flow location location data loss measuring people prediction privacy reports restaurant sensitive statistics temporal traffic