CONTAIN: Privacy-oriented Contact Tracing Protocols for Epidemics

Public health agencies advocate the use of contact tracing procedures to deal with pandemics such as COVID-19 to prevent the infection of a vast population. Although several mobile applications have been developed previously for contact tracing, they typically require collection of privacy-intrusive information such as GPS locations, personal data, or require infrastructures such as WiFi APs. In this paper, we introduce CONTAIN, an early proposal for privacy-sensitive contact tracing. CONTAIN is a privacy-oriented bluetooth-based mobile digital contact tracing framework that does not rely on any infrastructure-based location sensing, nor the continuous logging of personally identifiable information. The goal of CONTAIN is to allow users to determine with complete privacy if and when they have been within a short distance of someone that is infected. We identify and prove the privacy guarantees provided by CONTAIN. We also present a simulation study utilizing an empirical trace dataset which shows that users can maximize their possibility of identifying if they were near an infected user by turning on the app in more crowded settings.