  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.
