Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release
arXiv:2004.07442
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.
arXiv commentThe paper has been accepted by the IEEE International Conference on Multimedia & Expo 2020(ICME 2020)
References in corpus (5)
- VoxCeleb2: Deep Speaker Recognition
- VoxCeleb: a large-scale speaker identification dataset
- Geo-Indistinguishability: Differential Privacy for Location-Based Systems
- The GDPR & Speech Data: Reflections of Legal and Technology Communities, First Steps towards a Common Understanding
- Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?