  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.
