# Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release

arXiv: https://arxiv.org/abs/2004.07442 · HTML: https://mlsanity.com/paper/2004.07442/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [The VoicePrivacy 2020 Challenge: Results and findings](https://mlsanity.com/paper/2109.00648/) (2021)
- [Differentially Private Speaker Anonymization](https://mlsanity.com/paper/2202.11823/) (2022)
- [A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness](https://mlsanity.com/paper/2212.09006/) (2022)
- [Learning De-identified Representations of Prosody from Raw Audio](https://mlsanity.com/paper/2107.08248/) (2021)
- [Mitigating Unauthorized Speech Synthesis for Voice Protection](https://mlsanity.com/paper/2410.20742/) (2024)
- [SAIC: Integration of Speech Anonymization and Identity Classification](https://mlsanity.com/paper/2312.15190/) (2023)
- [Defend Data Poisoning Attacks on Voice Authentication](https://mlsanity.com/paper/2209.04547/) (2022)
- [DeID-VC: Speaker De-identification via Zero-shot Pseudo Voice Conversion](https://mlsanity.com/paper/2209.04530/) (2022)
- [Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples](https://mlsanity.com/paper/2211.05446/) (2022)
- [ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation](https://mlsanity.com/paper/2406.03404/) (2024)
- [Interpolation-Based Optimization for Enforcing lp-Norm Metric Differential Privacy in Continuous and Fine-Grained Domains](https://mlsanity.com/paper/2601.09946/) (2026)
- [Dial HEALTHDIAL for Advice: A Multilingual and Multi-Parallel Spoken Dialogue Dataset for Knowledge-Grounded Information Seeking](https://mlsanity.com/paper/2605.30107/) (2026)

## Abstract (arXiv)

With the development of smart devices, such as the Amazon Echo and Apple's HomePod, speech data have become a new dimension of big data. However, privacy and security concerns may hinder the collection and sharing of real-world speech data, which contain the speaker's identifiable information, i.e., voiceprint, which is considered a type of biometric identifier. Current studies on voiceprint privacy protection do not provide either a meaningful privacy-utility trade-off or a formal and rigorous definition of privacy. In this study, we design a novel and rigorous privacy metric for voiceprint privacy, which is referred to as voice-indistinguishability, by extending differential privacy. We also propose mechanisms and frameworks for privacy-preserving speech data release satisfying voice-indistinguishability. Experiments on public datasets verify the effectiveness and efficiency of the proposed methods.