PrivyTRAC: Privacy and Security Preserving Contact Tracing System

TL;DR

PrivyTRAC is a privacy-preserving contact tracing system that enhances accuracy by considering environmental virus effects and user privacy, outperforming traditional proximity-based methods in infection risk estimation.

Contribution

It introduces a novel privacy-preserving contact tracing system that incorporates environmental virus effects and anonymized risk maps for improved accuracy.

Findings

PrivyTRAC reduces false positives compared to proximity methods.

It effectively preserves user privacy without transmitting personal data.

Incorporating test results further improves infection risk estimation.

Abstract

Smartphone location-based methods have been proposed and implemented as an effective alternative to traditional labor intensive contact tracing methods. However, there are serious privacy and security concerns that may impede wide-spread adoption in many societies. Furthermore, these methods rely solely on proximity to patients, based on Bluetooth or GPS signal for example, ignoring lingering effects of virus, including COVID-19, present in the environment. This results in inaccurate risk assessment and incomplete contact tracing. A new system concept, called PrivyTRAC, preserves user privacy, increases security and improves accuracy of smartphone contact tracing. PrivyTRAC enhances users' and patients' privacy by letting users conduct self-evaluation based on the risk maps download to their smartphones. No user information is transmitted to external locations or devices, and no personally identifiable patient information is embedded in the risk maps as they are processed anonymized and aggregated locations of confirmed patients. The risk maps consider both spatial proximity and temporal effects to improve the accuracy of the infection risk estimation. Experiments conducted in the paper illustrate improvement of PrivyTRAC over proximity based methods in terms of true and false positives. An approach to further improve infection risk estimation by incorporating both positive and negative local test results from contacts of confirmed cases is also described.