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• W3016661275 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." @default.
• W3016661275 created "2020-04-24" @default.
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• W3016661275 date "2020-04-15" @default.
• W3016661275 modified "2023-09-26" @default.
• W3016661275 title "Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release" @default.
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• W3016661275 doi "https://doi.org/10.48550/arxiv.2004.07442" @default.
• W3016661275 hasPublicationYear "2020" @default.
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