Zaidi Abbas, Ahuja Ritesh, Shahabi Cyrus
USC Information Laboratory, University of Southern California, Los Angeles, USA.
IEEE Int Conf Mob Data Manag. 2022 Jun;2022:361-366. doi: 10.1109/mdm55031.2022.00081. Epub 2022 Aug 25.
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.
准确监测建筑物内的人员数量对于限制新冠病毒传播至关重要。由于隐私担忧，接触者追踪应用的低采用率增加了被动数字追踪替代方案的普及程度。大量的WiFi接入点可以方便地追踪大学和企业园区内的移动设备。南加州大学采用的CrowdMap系统通过收集校园周边接入点连接的汇总统计数据来实现这种追踪。然而，由于这些设备可用于推断个人的行动，即使是汇总的占用统计数据仍存在严重风险，即会侵犯个人的位置隐私。我们研究了使用差分隐私来报告该系统的统计数据，这些数据通过点计数查询和范围计数查询来衡量。我们提出了离散化方案，仅根据用户与WiFi接入点的连接来对用户位置进行建模。利用这些信息，我们能够在将对单个用户隐私的风险降至最低的情况下，准确统计校园建筑区域（如实验室、走廊和大型讨论厅）内的居住人数。
