# The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment

arXiv: https://arxiv.org/abs/2005.09413 · HTML: https://mlsanity.com/paper/2005.09413/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 6 of 6)

- [The VoicePrivacy 2020 Challenge: Results and findings](https://mlsanity.com/paper/2109.00648/) (2021)
- [Bias in Automated Speaker Recognition](https://mlsanity.com/paper/2201.09486/) (2022)
- [The Third VoicePrivacy Challenge: Preserving Emotional Expressiveness and Linguistic Content in Voice Anonymization](https://mlsanity.com/paper/2601.11846/) (2026)
- [You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks](https://mlsanity.com/paper/2506.09521/) (2025)
- [VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks](https://mlsanity.com/paper/2509.18413/) (2025)
- [The distribution of calibrated likelihood functions on the probability-likelihood Aitchison simplex](https://mlsanity.com/paper/2509.03365/) (2025)

## Abstract (arXiv)

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.