ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (10): 2201-2208.doi: 10.7544/issn1000-1239.2020.20200474

Special Issue: 2020密码学与数据隐私保护研究专题

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Template Protection of Speaker Recognition Based on Random Mapping Technology

Ding Yong1,4,5, Li Jiahui2,4, Tang Shijie1,3, Wang Huiyong2,4   

  1. 1(School of Computer Science & Information Security, Guilin University of Electronic Technology, Guilin, Guangxi 541004);2(School of Mathematics & Computing Science, Guilin University of Electronic Technology, Guilin, Guangxi 541004);3(School of Electronic Engineer & Automation, Guilin University of Electronic Technology, Guilin, Guangxi 541004);4(Guangxi Key Laboratory of Cryptography and Information Security (Guilin University of Electronic Technology), Guilin, Guangxi 541004);5(Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, Guangdong 518055)
  • Online:2020-10-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61772150, 61862012, 61962012), the Guangxi Key Research and Development Program (AB17195025), the Guangxi Natural Science Foundation (2018GXNSFDA281054, 2018GXNSFAA281232, 2019GXNSFFA245015, 2019GXNSFGA245004, AD19245048), and the Peng Cheng Laboratory Project of Guangdong Province (PCL2018KP004).

Abstract: Speaker recognition realizes a simple and fast biometric authentication method which is non-contact, not easy to forge and can be authenticated remotely, However, it is not completely secure, as in the process of authentication, storing user data in a third party brings many security and privacy concerns. In order to mitigate the challenge, the template protection of speaker recognition problem is studied based on identity vector (i-Vector) and linear discriminant analysis (LDA), and an improved random mapping technique is proposed, which is used to randomize the voiceprint features. Then we construct a template protection scheme for speaker recognition, which allows users to register in random domain and to complete speaker recognition. Finally, we use the open Chinese speech data set (AISHELL) to simulate the proposed scheme. The results show that the scheme does not significantly affect the accuracy of voiceprint authentication, and realize confidential comparisons of voiceprint templates, which effectively ensures the security and privacy of voice data in speaker recognition.

Key words: random mapping, orthogonal matrix, random domain, speaker recognition, privacy protection

CLC Number: