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.

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. 

CrowdMap is an anonymous occupancy monitoring system developed in response to the COVID-19 pandemic. CrowdMap collects, cleans, and visualizes occupancy data derived from connection logs generated by large arrays of Wi-Fi access points. Thus, CrowdMap is a passive digital tracking tool that can be used to reopen buildings safely, as it helps actively manage occupancy limits and identify utilization trends at scale. Occupancy monitoring is possible at various levels of resolution over large spatial (e.g., from individual rooms to entire buildings) and temporal (e.g., from hours to months) extents. The CrowdMap web-based front-end implements powerful spatiotemporal querying and visualization tools to quickly and effectively explore occupancy patterns throughout large campuses. We will demonstrate CrowdMap and its spatiotemporal GUI that was deployed for an entire university campus with data continuously being collected since summer 2020.

Sitao Min

Papers

CrowdMap: Spatiotemporal Visualization of Anonymous Occupancy Data for Pandemic Response