TY - UNPB
T1 - Safer Illinois and RokWall: Privacy Preserving University Health Apps for COVID-19
AU - Mailthody, Vikram Sharma
AU - Wei, James
AU - Chen, Nicholas
AU - Behnia, Mohammad
AU - Yao, Ruihao
AU - Wang, Qihao
AU - Agrawal, Vedant
AU - He, Churan
AU - Wang, Lijian
AU - Chen, Leihao
AU - Agarwal, Amit
AU - Richter, Edward
AU - Hwu, Wen-Mei
AU - Fletcher, Christopher
AU - Xiong, Jinjun
AU - Miller, Andrew
AU - Patel, Sanjay
PY - 2021/1/19
Y1 - 2021/1/19
N2 - 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.
AB - 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.
KW - COVID-19
KW - severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
KW - Novel coronavirus
KW - Coronavirus
KW - 2019-nCoV
KW - Pandemic
M3 - Working paper
BT - Safer Illinois and RokWall: Privacy Preserving University Health Apps for COVID-19
PB - arXiv
ER -