Environmental Adaptive Privacy Preserving Contact Tracing System for Respiratory Infectious Diseases.

The COVID-19 pandemic has made the scientific community devise means to implement “contact tracing” mechanisms to mitigate the spread of the infection. The crucial idea is to scan and record close contacts between users using mobile devices, in order to notify persons when their close contact(s) is diagnosed positive. Current contact tracing systems’ false-positive rate is too high to be practical as they do not filter Bluetooth scan results outside range of infection. Furthermore current systems neglect airborne transmission other than droplet transmission. Moreover, the ability granted to service providers of the contact tracing systems to access user data violates user privacy. Finally, attackers can modify, remove or fabricate contact records in their devices, which harms the integrity of the system. In this paper, we propose and develop a new contact tracing system which uses environmental factors to filter out results outside estimated effective transmission distance, and also take airborne transmission into consideration. In addition, we implement a rerandomizable signature scheme with blockchain bulletin board to provide confidentiality and integrity. We also evaluate the performance of our theory by implementing our algorithm on mobile devices.