Differentially Private Occupancy Monitoring from WiFi Access Points

Accurately monitoring the number of individuals inside a building is vital to limiting COVID-19 transmission. Low adoption of contact tracing apps due to privacy concerns has increased pervasiveness of passive digital tracking alternatives. Large arrays of WiFi access points can conveniently track mobile devices on university and industry campuses. The CrowdMap system employed by the University of Southern California enables such tracking by collecting aggregate statistics from connections to access points around campus. However, since these devices can be used to infer the movement of individuals, there is still a significant risk that even aggregate occupancy statistics will violate the location privacy of individuals. We examine the use of Differential Privacy in reporting statistics from this system as measured using point and range count queries. We propose discretization schemes to model the positions of users given only user connections to WiFi access points. Using this information we are able to release accurate counts of occupants in areas of campus buildings such as labs, hallways, and large discussion halls with minimized risk to individual users' privacy.

Automated summary

Accurately monitoring the number of individuals inside a building is crucial in controlling COVID-19 transmission. Due to privacy concerns, the low adoption of contact tracing apps has led to the prevalence of passive digital tracking alternatives. The use of large arrays of WiFi access points makes it convenient to track mobile devices on university and industry campuses. However, there is still a risk of violating individual location privacy even with aggregate occupancy statistics. This study examines the use of Differential Privacy in reporting statistics and proposes discretization schemes to minimize the risk to individual users' privacy.