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COVID-19大流行。个人的实时位置跟踪可用于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。 COVID-19大流行的紧迫性要求在隐私保护与公共健康利益之间保持谨慎的平衡。 该项目旨在开发技术和移动的应用程序REACT，通过隐私增强的用户位置和症状跟踪进行实时接触跟踪和风险监测。 为了增强隐私，用户可以控制和细化收集和使用其信息的频率和粒度。 REACT将能够：1）追踪确诊病例接触过的个人和地点，进行隔离和消毒，以控制进一步传播; 2）根据他们访问的地点和与其他人的接触进行个人风险监测，以便他们能够获得信息和警报;以及3）社区风险监测和发现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）在大量用户和多分辨率位置跟踪的情况下，用于联系人跟踪查询的高效和可扩展的数据结构和算法; 2）用于真实的时间建模用户风险的基于学习的方法; 3）用于监测社区风险的社会网络传感器方法，以及4）多阶段隐私方法，其中用户可以上传一般化位置以接收警报，并且当他们被通知为可能与确诊病例接触时上传更精确的位置以进行细化和确认。 将研究更严格的隐私增强措施，包括基于地理不可否认性及其变体的位置扰动，以及用于加密位置上的接触跟踪的可搜索加密。 此外，该项目还包括一系列传播和教育活动：1）发布一个具有开源系统组件的移动的应用程序; 2）调查隐私保护机制，以分享收集到的研究数据;和3）将研究融入课堂，组织系列研讨会和辅导，以促进跨学科研究领域。该奖项反映了NSF的法定使命，并被认为是值得的通过使用基金会的知识价值和更广泛的影响审查标准进行评估，

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.