The Privacy ZEBRA: Zero Evidence Biometric Recognition Assessment
arXiv:2005.09413 · doi:10.21437/Interspeech.2020-1815
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.
arXiv commentsubmitted to Interspeech 2020
References in corpus (5)
- General Framework to Evaluate Unlinkability in Biometric Template Protection Systems
- Introducing the VoicePrivacy Initiative
- The GDPR & Speech Data: Reflections of Legal and Technology Communities, First Steps towards a Common Understanding
- The PAV algorithm optimizes binary proper scoring rules
- The VoicePrivacy 2022 Challenge Evaluation Plan
Cited by in corpus (7)
- The VoicePrivacy 2020 Challenge: Results and findings
- Bias in Automated Speaker Recognition
- A False Sense of Privacy: Towards a Reliable Evaluation Methodology for the Anonymization of Biometric Data
- VoicePAT: An Efficient Open-source Evaluation Toolkit for Voice Privacy Research
- Privacy-oriented manipulation of speaker representations
- Benchmarking and challenges in security and privacy for voice biometrics
- A Tandem Framework Balancing Privacy and Security for Voice User Interfaces