With the introduction of cybertwin, a new approach to represent human or things in the cyberspace, it is foreseeable that vehicles will be able to offer more and more services in the future. Naturally, considering the safety of drivers, speaker recognition will be widely used in vehicle scenarios. Speaker recognition technologies are experiencing increasing popularity due to the unique and indissoluble link between individuals and their voices. However, the coming cybertwin-driven 6G brings speaker recognition technologies unprecedented challenges, especially in preventing the disclosure of voiceprint. To address these challenges, an efficient and privacy-preserving speaker recognition scheme for cybertwin-driven 6G, referred to as NEATEN, is proposed in this article. With NEATEN, the speaker identity can be recognized at multiple security levels without leaking the voiceprint data. More concretely, based on the random projection data perturbation, voiceprint perturbation algorithms in two phases and the corresponding ciphertext-based similarity computation algorithm are proposed. By using these algorithms, our efficient and accurate speaker recognition scheme can be achieved. Orthogonal to the previous works of biometric identification based on the Euclidean distance, NEATEN makes progress on the non-Euclidean distance, such as cosine distance and complicated distance. Detailed analysis shows that NEATEN can resist various known security threats. Experiments conducted on TIMIT and Voxceleb data sets have demonstrated that NEATEN is highly accurate and efficient, and can be flexibly deployed in a real cybertwin-driven 6G vehicle environment.

中文翻译：

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Cyber​​twin 驱动的 6G 的高效且隐私保护的说话人识别

随着赛博双胞胎这一在网络空间中代表人或物的新方法的引入，可以预见未来车辆将能够提供越来越多的服务。自然，考虑到驾驶员的安全，说话人识别将广泛应用于车辆场景。由于个人与其声音之间独特且不可分割的联系，说话人识别技术正越来越受欢迎。然而，即将到来的网络孪生驱动的6G给说话人识别技术带来了前所未有的挑战，尤其是在防止声纹泄露方面。为了应对这些挑战，本文提出了一种用于网络孪生驱动的 6G 的高效且保护隐私的说话人识别方案，称为 NEATEN。与 NEATEN，可以在多个安全级别识别说话人身份，而不会泄漏声纹数据。更具体地说，基于随机投影数据扰动，提出了两阶段声纹扰动算法和相应的基于密文的相似度计算算法。通过使用这些算法，我们可以实现高效准确的说话人识别方案。与以往基于欧氏距离的生物特征识别工作正交，NEATEN在非欧距离上取得了进展，如余弦距离和复杂距离。详细分析表明，NEATEN可以抵御各种已知的安全威胁。在 TIMIT 和 Voxceleb 数据集上进行的实验证明 NEATEN 具有高度的准确性和效率，