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Author

Yingzhe Liu

Bio: Yingzhe Liu is an academic researcher from University of Southern California. The author has contributed to research in topics: Occupancy. The author has co-authored 1 publications.
Topics: Occupancy

Papers
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Proceedings ArticleDOI
02 Nov 2021
TL;DR: CrowdMap as discussed by the authors is an anonymous occupancy monitoring system developed in response to the COVID-19 pandemic, which collects, cleans, and visualizes occupancy data derived from connection logs generated by large arrays of Wi-Fi access points.
Abstract: 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.

2 citations


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Proceedings ArticleDOI
01 Jun 2022
TL;DR: Using discretization schemes to model the positions of users given only user connections to WiFi access points, this work is 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.
Abstract: 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.

1 citations

Proceedings ArticleDOI
01 Jun 2022
TL;DR: In this article , the authors examined the use of differential privacy in reporting statistics from the CrowdMap system as measured using point and range count queries, and proposed discretization schemes to model the positions of users given only user connections to WiFi access points.
Abstract: 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.