Safer Illinois and RokWall: Privacy Preserving University Health Apps for COVID-19

Mailthody, Vikram Sharma; Wei, James; Chen, Nicholas; Behnia, Mohammad; Yao, Ruihao; Wang, Qihao; Agrawal, Vedant; He, Churan; Wang, Lijian; Chen, Leihao; Agarwal, Amit; Richter, Edward; Hwu, Wen-Mei; Fletcher, Christopher W.; Xiong, Jinjun; Miller, Andrew; Patel, Sanjay

COVID-19 has fundamentally disrupted the way we live. Government bodies, universities, and companies worldwide are rapidly developing technologies to combat the COVID-19 pandemic and safely reopen society. Essential analytics tools such as contact tracing, super-spreader event detection, and exposure mapping require collecting and analyzing sensitive user information. The increasing use of such powerful data-driven applications necessitates a secure, privacy-preserving infrastructure for computation on personal data. In this paper, we analyze two such computing infrastructures under development at the University of Illinois at Urbana-Champaign to track and mitigate the spread of COVID-19. First, we present Safer Illinois, a system for decentralized health analytics supporting two applications currently deployed with widespread adoption: digital contact tracing and COVID-19 status cards. Second, we introduce the RokWall architecture for privacy-preserving centralized data analytics on sensitive user data. We discuss the architecture of these systems, design choices, threat models considered, and the challenges we experienced in developing production-ready systems for sensitive data analysis.