Development of a Privacy-preserving Infection Risk Assessment Protocol

In light of the recent COVID-19 pandemic, contact tracing has been found an effective measure to mitigate the spread of the corresponding SARS-CoV-2 virus. The aim of contact tracing is to identify, notify and potentially quarantine persons who have been in close contact with infected individuals, thereby breaking the chain of infections. Next to manual contact tracing usually performed by local health authorities, several automatic, technology-driven contact tracing systems have been proposed throughout the last year, including popular constructions such as the Google/Apple Exposure Notification Framework (GAEN), DP-3T, or PACT. However, recent research has found that most of these constructions are still far from perfect, as they are susceptible to potential attacks (e.g., relay/replay of information) or they disallow collection of valuable data such as statistics or location-information due to privacy concern. Hence, our objective is to propose a novel contact tracing system which overcomes these shortcomings of previous constructions with the help of applied cryptography while maintaining a high degree of user privacy and real-world applicability.

Faculty Supervisor:

Florian Kerschbaum

Student:

Partner:

Rheinisch-Westfälische Technische Hochschule Aachen

Discipline:

Computer science

Sector:

Education

University:

University of Waterloo

Program: