The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment

TL;DR

The paper introduces the ZEBRA framework with two new privacy metrics to evaluate privacy preservation in biometric recognition, especially in speech applications, addressing the need for privacy-aware assessment tools.

Contribution

It presents the first privacy metrics for biometric recognition evaluation, specifically designed to measure privacy preservation levels in speech technology.

Findings

Demonstrates application within VoicePrivacy challenge

Provides metrics for average and worst-case privacy disclosure

Framework adaptable to broader biometric privacy assessments

Abstract

Mounting privacy legislation calls for the preservation of privacy in speech technology, though solutions are gravely lacking. While evaluation campaigns are long-proven tools to drive progress, the need to consider a privacy adversary implies that traditional approaches to evaluation must be adapted to the assessment of privacy and privacy preservation solutions. This paper presents the first step in this direction: metrics. We introduce the zero evidence biometric recognition assessment (ZEBRA) framework and propose two new privacy metrics. They measure the average level of privacy preservation afforded by a given safeguard for a population and the worst-case privacy disclosure for an individual. The paper demonstrates their application to privacy preservation assessment within the scope of the VoicePrivacy challenge. While the ZEBRA framework is designed with speech applications in mind, it is a candidate for incorporation into biometric information protection standards and is readily extendable to the study of privacy in applications even beyond speech and biometrics.