论文标题
语音可区分性：保护隐私性语音数据中的语音纹
Voice-Indistinguishability: Protecting Voiceprint in Privacy-Preserving Speech Data Release
论文作者
论文摘要
随着Amazon Echo和Apple HomePod等智能设备的开发，语音数据已成为大数据的新维度。但是，隐私和安全问题可能会阻碍现实世界中的语音数据的收集和共享，这些语音数据包含说话者可识别的信息，即语音纹理，该信息被认为是一种生物识别标识符。当前关于语音纹理隐私保护的研究不能提供有意义的隐私性权衡折衷或对隐私的正式和严格的定义。在这项研究中，我们为语音纹理隐私设计了一种新颖而严格的隐私度量，该指标通过扩展差异隐私而被称为语音可区分性。我们还提出了用于保存隐私语音数据的机制和框架，以满足语音可区分性。公共数据集上的实验验证了所提出方法的有效性和效率。
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.
