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.
