Quantifying the Privacy-Utility Trade-offs in COVID-19 Contact Tracing Apps

TL;DR

This paper reviews COVID-19 contact tracing methods, classifies their characteristics, and introduces a new approach for evaluating privacy-utility trade-offs using qualitative and quantitative measures across different utility levels.

Contribution

It presents a novel privacy evaluation framework for contact tracing apps, incorporating both qualitative and quantitative assessments at multiple utility levels.

Findings

Proposed a new privacy-utility assessment method.

Classified contact tracing techniques based on technology and privacy trade-offs.

Provided insights into balancing privacy and utility in COVID-19 apps.

Abstract

How to contain the spread of the COVID-19 virus is a major concern for most countries. As the situation continues to change, various countries are making efforts to reopen their economies by lifting some restrictions and enforcing new measures to prevent the spread. In this work, we review some approaches that have been adopted to contain the COVID-19 virus such as contact tracing, clusters identification, movement restrictions, and status validation. Specifically, we classify available techniques based on some characteristics such as technology, architecture, trade-offs (privacy vs utility), and the phase of adoption. We present a novel approach for evaluating privacy using both qualitative and quantitative measures of privacy-utility assessment of contact tracing applications. In this new method, we classify utility at three (3) distinct levels: no privacy, 100% privacy, and at k where k is set by the system providing the utility or privacy.