说话人识别的同态加密：生物特征的保护 模板和供应商模型参数

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摘要翻译：
在处理生物特征数据时，数据隐私是至关重要的。根据最新的欧洲数据隐私法规和支付服务指令，生物识别模板保护对任何商业应用都至关重要。为了确保生物识别服务运营商之间的不可链接性、泄露的加密模板的不可逆转性以及例如语音模型的可更新性，根据i-vector范式，生物识别语音系统为欧盟最新的数据隐私立法做好了准备。使用Paillier密码系统，欧几里得和余弦比较器已知，以确保数据隐私要求，而不损失识别和校准性能。为了弥补从模板保护到说话人识别之间的差距，本文提出了两种用于双协方差比较器的结构，作为一种生成模型。第一个体系结构保护生物特征数据捕获主体的隐私。在第二体系结构中，比较器的模型参数也被加密，使得生物测定服务提供商可以向多个生物测定服务运营商提供使用不同密钥对的相同比较模块。对2013-2014年NIST I-向量机学习挑战赛的数据进行了概念验证和复杂性分析。
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英文标题：
《Homomorphic Encryption for Speaker Recognition: Protection of Biometric
  Templates and Vendor Model Parameters》
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作者：
Andreas Nautsch, Sergey Isadskiy, Jascha Kolberg, Marta Gomez-Barrero,
  Christoph Busch
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Cryptography and Security        密码学与安全
分类描述：Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.
涵盖密码学和安全的所有领域，包括认证、公钥密码系统、携带证明的代码等。大致包括ACM主题课程D.4.6和E.3中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Sound        声音
分类描述：Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
涵盖了声音计算的各个方面，以及声音作为一种信息通道。包括声音模型、分析和合成、音频用户界面、数据的可听化、计算机音乐和声音信号处理。包括ACM学科类H.5.5，并与H.1.2、H.5.1、H.5.2、I.2.7、I.5.4、I.6.3、J.5、K.4.2交叉。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Audio and Speech Processing        音频和语音处理
分类描述：Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome.  Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval;  audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.
处理代表音频、语音和语言的信号的理论和方法及其应用。这包括分析、合成、增强、转换、分类和解释这些信号，以及相关信号处理系统的设计、开发和评估。机器学习和模式分析应用于上述任何领域也是受欢迎的。感兴趣的具体主题包括：听觉建模和助听器；声波束形成与声源定位；声场景分类；说话人分离；有源噪声控制和回声消除；增强；去混响；生物声学；音乐信号的分析、合成与修饰；音乐信息检索；多媒体音频和联合音视频处理；口语和书面语建模、切分、标注、句法分析、理解和翻译；文本挖掘；言语产生、感知和心理声学；语音分析、合成、感知建模和编码；鲁棒语音识别；说话人识别与特征描述；应用于语音、音频和语言信号的深度学习、在线学习和图形模型；以及从系统架构到快速算法的实现方面。
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英文摘要：
  Data privacy is crucial when dealing with biometric data. Accounting for the latest European data privacy regulation and payment service directive, biometric template protection is essential for any commercial application. Ensuring unlinkability across biometric service operators, irreversibility of leaked encrypted templates, and renewability of e.g., voice models following the i-vector paradigm, biometric voice-based systems are prepared for the latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean and cosine comparators are known to ensure data privacy demands, without loss of discrimination nor calibration performance. Bridging gaps from template protection to speaker recognition, two architectures are proposed for the two-covariance comparator, serving as a generative model in this study. The first architecture preserves privacy of biometric data capture subjects. In the second architecture, model parameters of the comparator are encrypted as well, such that biometric service providers can supply the same comparison modules employing different key pairs to multiple biometric service operators. An experimental proof-of-concept and complexity analysis is carried out on the data from the 2013-2014 NIST i-vector machine learning challenge.
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PDF链接：
https://arxiv.org/pdf/1803.03559

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关键词：供应商 Architecture Cryptography localization cancellation protection 指令 模型 服务 概念