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

TL;DR

This paper introduces a new privacy metric called voice-indistinguishability for protecting speaker identity in speech data, along with mechanisms to ensure privacy while maintaining data utility, verified through experiments.

Contribution

It proposes a formal privacy definition for voiceprint protection and develops mechanisms for privacy-preserving speech data release based on this metric.

Findings

The proposed methods effectively protect voiceprint privacy.

Experiments show the mechanisms are efficient and maintain data utility.

Voice-indistinguishability extends differential privacy to speech data.

Abstract

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.