Early THaW research on contact tracing is finding new relevance as groups across the US and around the world scramble to develop privacy-preserving contact-tracing apps. Notable app efforts include DP-3T, PEPP-PT, and SafePaths. All of those efforts focus on privacy-preserving apps for retrospective notification of persons who may have had “contact” with a person later determined to be ill with an infectious disease, where “contact” occurs when spending time in close proximity to the infected person. THaW student Aarathi Prasad went further, devising a system that could also detect “close encounters”, e.g., for those who may have visited a place soon after the infected person left. Some diseases, including perhaps the coronavirus, can linger in the air or on surfaces for hours.
Aarathi Prasad and David Kotz. ENACT: Encounter-based Architecture for Contact Tracing. Proceedings of the ACM Workshop on Physical Analytics (WPA), pages 37–42. ACM Press, June 2017. doi:10.1145/3092305.3092310. ©Copyright ACM.
Abstract: Location-based sharing services allow people to connect with others who are near them, or with whom they shared a past encounter. Suppose it were also possible to connect with people who were at the same location but at a different time – we define this scenario as a close encounter, i.e., an incident of spatial and temporal proximity. By detecting close encounters, a person infected with a contagious disease could alert others to whom they may have spread the virus. We designed a smartphone-based system that allows people infected with a contagious virus to send alerts to other users who may have been exposed to the same virus due to a close encounter. We address three challenges: finding devices in close encounters with minimal changes to existing infrastructure, ensuring authenticity of alerts, and protecting privacy of all users. Finally, we also consider the challenges of a real-world deployment.