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Secure Speaker Recognition System Using Homomorphic Encryption

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Innovative Security Solutions for Information Technology and Communications (SecITC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12596))

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Abstract

The ability to store and operate on cloud data provides flexibility and reduced hardware needs, but it has the disadvantage of a possible loss of data privacy. Homomorphic encryption solves this problem allowing operations on encrypted data to be performed, while maintaining its confidentiality. In this paper we focus on the introduction of homomorphic cryptosystems within neural networks. The main contribution is an implementation of a speaker recognition system whose security is based on the principles of homomorphic encryption. The application uses a convolutional neural network to classify encrypted spectral samples and it achieves an accuracy of over 99.5%. Moreover, we test different approximations for transfer functions analyzing time and memory consumption along accuracy.

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Authors and Affiliations

  1. “Ferdinand I” Military Technical Academy, 39-49 George Coşbuc Blvd., Bucharest, Romania

    Mihai-Cristian Chindriş, Mihai Togan & Ştefan-Ciprian Arseni

Authors
  1. Mihai-Cristian Chindriş
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  2. Mihai Togan
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  3. Ştefan-Ciprian Arseni
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Corresponding author

Correspondence to Ştefan-Ciprian Arseni .

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Editors and Affiliations

  1. Advanced Technologies Institute, Bucharest, Romania

    Diana Maimut

  2. Advanced Technologies Institute, Bucharest, Romania

    Andrei-George Oprina

  3. Faculté des Sciences et Techniques, University of Limoges, Limoges, France

    Damien Sauveron

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Chindriş, MC., Togan, M., Arseni, ŞC. (2021). Secure Speaker Recognition System Using Homomorphic Encryption. In: Maimut, D., Oprina, AG., Sauveron, D. (eds) Innovative Security Solutions for Information Technology and Communications. SecITC 2020. Lecture Notes in Computer Science(), vol 12596. Springer, Cham. https://doi.org/10.1007/978-3-030-69255-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-69255-1_13

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