Differentially Private Occupancy Monitoring from WiFi Access Points
Differentially Private Occupancy Monitoring from WiFi Access Points
复制标题
DOI：
10.1109/mdm55031.2022.00081
复制
发表时间：
2022-06
期刊：
影响因子：
--
通讯作者：
Abbas Zaidi;Ritesh Ahuja;C. Shahabi
中科院分区：
文献类型：
--
作者：
Abbas Zaidi;Ritesh Ahuja;C. Shahabi
准确监视建筑物内部的个体数量对于限制Covid-19传输至关重要。由于关注隐私而导致的接触跟踪应用程序的采用较低，因此被动数字跟踪替代方案的普遍性增加。大量的WiFi访问点可以方便地跟踪大学和行业校园的移动设备。南加州大学采用的人群图系统通过从连接到校园周围的接入点收集总统计数据来实现此类跟踪。但是，由于这些设备可用于推断个人的运动，因此即使是总占用统计数据也会侵犯个人的位置隐私，仍然存在很大的风险。我们研究了使用点和范围计数查询测量的差异隐私在报告该系统报告统计信息中的使用。我们提出了离散化方案，以对仅给定用户连接到WiFi访问点的用户的位置进行建模。使用这些信息，我们能够在校园建筑物（例如实验室，走廊和大型讨论大厅）中释放准确的居民计数，这些校园对个人用户隐私的风险降至最低。
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.