Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release
Year: 2020
Type: preprint
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.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Next generation multilingual End-to-End speech recognition (from G30 to G200), Research on basic technology and application of CDMS to realize a democratic data distribution society, Software Foundation for Interoperability of Autonomic Distributed Data based on Bidirectional Transformation, Achieving Differential Privacy under Spatiotemporal Correlations
OpenAlex ID: W3016661275