RAPID: Collaborative: REACT: Real-time Contact Tracing and Risk Monitoring via Privacy-enhanced Mobile Tracking

接触者追踪对于控制流行病爆发至关重要，例如快速增长的新冠肺炎大流行。可以使用个人的实时位置跟踪来显著加快和扩大接触者追踪。然而，这种使用也加剧了对个人隐私和数据滥用的担忧。新冠肺炎疫情的紧迫性要求在隐私保护和公共健康利益之间谨慎平衡。该项目旨在开发技术和移动应用程序Reaction，通过对用户位置和症状的隐私增强跟踪，实现实时接触者跟踪和风险监控。为了增强隐私，用户可以控制和细化收集和使用他们的信息的频率和粒度。REPACT将实现：1)对确诊病例与之有接触的个人和地点进行追踪，以便进行隔离和净化，以控制进一步的传播；2)根据他们访问的地点和他们与他人的接触进行个人风险监测，以便他们能够得到通知和警报；以及3)社区风险监测和检测社区传播的早期信号，为更大规模的感染做好准备。该项目将开发和研究：1)高效和可扩展的数据结构和算法，用于在给定大量用户和多分辨率位置跟踪的情况下进行接触者追踪查询；2)基于学习的方法，用于实时建模用户的风险；3)社交网络传感器方法，用于监测社区风险；以及4)多阶段隐私方法，其中用户可以上载通用位置以接收警报，并在被通知为可能与确诊病例接触的人时上载更精确的位置，以进行完善和确认。将研究更严格的隐私增强措施，包括基于地理不可分辨及其变体的位置扰动，以及用于在加密位置上追踪联系人的可搜索加密。此外，该项目还包括一系列宣传和教育活动：1)发布带有开源系统组件的移动应用程序；2)调查隐私保护机制，以共享收集的研究数据；3)在课堂上整合研究，组织研讨会系列和教程，以促进跨学科研究领域。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Contact tracing is critical for controlling epidemic disease outbreaks such as the fast-growing COVID-19 pandemic. Real-time location traces of individuals can be used to significantly speed up and scale up contact tracing. However, such use also heightens concerns on individual privacy and data abuse. Urgency of the COVID-19 pandemic requires a careful balance of privacy protection with public health benefits. This project aims to develop techniques and a mobile application, REACT, for REAal-time Contact Tracing and risk monitoring via privacy-enhanced tracking of users' locations and symptoms. To enhance privacy, users can control and refine the frequency and granularity with which their information will be collected and used. REACT will enable: 1) contact tracing of individuals and locations that a confirmed case has contact with for quarantine and decontamination to control further spread, 2) individual risk monitoring based on the locations they visit and their contact with others so they can be informed and alerted; and 3) community risk monitoring and detection of early signals of community spread to prepare for larger-scale infections. The project will develop and study: 1) efficient and scalable data structures and algorithms for contact tracing queries given a large number of users and multi-resolution location traces; 2) a learning-based approach for modeling users’ risks in real time; 3) a social network sensors approach for monitoring the community risk, and 4) a multi-stage privacy approach where users can upload generalized locations to receive alerts and more precise locations when they are notified as possible contacts with confirmed cases for refinement and confirmation. More rigorous privacy enhancements will be studied including location perturbation based on geo-indistinguishability and its variants and searchable encryption for contact tracing on encrypted locations. In addition, the project includes a set of dissemination and education activities: 1) releasing a mobile app with open-source system components; 2) investigating privacy-preserving mechanisms to share the collected data for research studies; and 3) integrating the research in classes and organizing seminar series and tutorials to promote the interdisciplinary research area.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.