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.

中文翻译：

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更安全的伊利诺伊州和RokWall：针对COVID-19的隐私保护大学健康应用程序

COVID-19从根本上破坏了我们的生活方式。全球的政府机构，大学和公司都在迅速开发技术来对抗COVID-19大流行并安全地重新开放社会。基本的分析工具（例如联系人跟踪，超级传播程序事件检测和暴露映射）需要收集和分析敏感的用户信息。这种功能强大的数据驱动应用程序的日益广泛使用，需要安全，可保护隐私的基础结构来对个人数据进行计算。在本文中，我们分析了伊利诺伊大学香槟分校正在开发的两个这样的计算基础架构，以跟踪和缓解COVID-19的传播。首先，我们介绍Safer Illinois，这是一种用于分散式健康分析的系统，可支持目前已广泛部署的两个应用程序：数字联系人跟踪和COVID-19状态卡。其次，我们介绍了RokWall体系结构，用于对敏感用户数据进行隐私保护的集中式数据分析。我们讨论了这些系统的体系结构，设计选择，考虑的威胁模型，以及我们在开发可用于敏感数据分析的生产就绪型系统时遇到的挑战。