Privacy-accuracy trade-offs in noisy digital exposure notifications

TL;DR

This paper examines the privacy-accuracy balance in digital exposure notifications, applying differential privacy principles to quantify how noise addition affects the accuracy of contact-tracing alerts.

Contribution

It introduces a formal analysis of privacy-accuracy trade-offs in exposure notification systems using differential privacy, translating existing privacy results to this context.

Findings

Naive bounds on accuracy loss due to privacy noise

Quantitative analysis of privacy-accuracy trade-offs

Application of differential privacy to exposure notifications

Abstract

Since the global spread of Covid-19 began to overwhelm the attempts of governments to conduct manual contact-tracing, there has been much interest in using the power of mobile phones to automate the contact-tracing process through the development of exposure notification applications. The rough idea is simple: use Bluetooth or other data-exchange technologies to record contacts between users, enable users to report positive diagnoses, and alert users who have been exposed to sick users. Of course, there are many privacy concerns associated with this idea. Much of the work in this area has been concerned with designing mechanisms for tracing contacts and alerting users that do not leak additional information about users beyond the existence of exposure events. However, although designing practical protocols is of crucial importance, it is essential to realize that notifying users about exposure events may itself leak confidential information (e.g. that a particular contact has been diagnosed). Luckily, while digital contact tracing is a relatively new task, the generic problem of privacy and data disclosure has been studied for decades. Indeed, the framework of differential privacy further permits provable query privacy by adding random noise. In this article, we translate two results from statistical privacy and social recommendation algorithms to exposure notification. We thus prove some naive bounds on the degree to which accuracy must be sacrificed if exposure notification frameworks are to be made more private through the injection of noise.