Paper 2020/375

Privacy-Preserving Contact Tracing of COVID-19 Patients

Leonie Reichert, Samuel Brack, and Björn Scheuermann

Abstract

The current Covid-19 pandemic shows that our modern globalized world can be heavily affected by a quickly spreading, highly infectious, deadly virus in a matter of weeks. It became apparent that manual contact tracing and quarantining of suspects can only be effective in the first days of the spread before the exponential growth overwhelms the health authorities. By automating tracing processes and quarantining everyone who came in contact with infected people, as well as arriving travelers, it should be possible to quickly loosen lockdown measures. Countries like China, Singapore and Israel hastily developed privacy-endangering schemes to computationally trace contacts using user-generated location histories or mass surveillance data . There have been reports of deanonymizations of South Korean citizens from the public “anonymized” data set of infected people. To approach this conflict of interests first identify and formulate of privacy risks of contact tracing. On this basis we propose a privacy-preserving approach to contact tracing using secure multi party computation and binary search. Our preliminary evaluation shows the idea is feasible in different scenarios derived from real-world case studies.