Privacy-Preserving Speaker Authentication

  • Manas Pathak
  • Jose Portelo
  • Bhiksha Raj
  • Isabel Trancoso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7483)


Speaker authentication systems require access to the voice of the user. A person’s voice carries information about their gender, nationality etc., all of which become accessible to the system, which could abuse this knowledge. The system also stores users’ voice prints – these may be stolen and used to impersonate the users elsewhere. It is therefore important to develop privacy preserving voice authentication techniques that enable a system to authenticate users by their voice, while simultaneously obscuring the user’s voice and voice patterns from the system. Prior work in this area has employed expensive cryptographic tools, or has cast authentication as a problem of exact match with compromised accuracy. In this paper we present a new technique that employs secure binary embeddings of feature vectors, to perform voice authentication in a privacy preserving manner with minimal computational overhead and little loss of classification accuracy.


Speaker Recognition Privacy Preserve Homomorphic Encryption Universal Background Model Target Speaker 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adler, A.: Biometric System Security. In: Jain, A.K., Flynn, P., Ross, A. (eds.) Handbook of Biometrics. Springer (2007)Google Scholar
  2. 2.
    Pathak, M., Raj, B.: Privacy Preserving Speaker Verification using adapted GMMs. In: Proc. Interspeech (2011)Google Scholar
  3. 3.
    Pathak, M., Raj, B.: Privacy-Preserving Speaker Verification as Password Matching. In: Proc. ICASSP (2012)Google Scholar
  4. 4.
    Boufounos, P., Rane, S.: Secure Binary Embeddings for Privacy Preserving Nearest Neighbors. In: Proc. Workshop on Information Forensics and Security, WIFS (2011)Google Scholar
  5. 5.
    Boufounos, P.: Universal Rate-Efficient Scalar Quantization. IEEE Trans. on Information Theory 58(3), 1861–1872 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Davis, S.B., Mermelstein, P.: Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing 28(4), 357–366 (1980)CrossRefGoogle Scholar
  7. 7.
    Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker Verification using Adapted Gaussian Mixture Models. Digital Signal Processing 10(1-3), 19–41 (2000)CrossRefGoogle Scholar
  8. 8.
    Kenny, P., Boulianne, G., Ouellet, P., Dumouchel, P.: Joint Factor Analysis Versus Eigenchannels in Speaker Recognition. IEEE Trans. Audio, Speech and Language Processing 15(4), 1435–1447 (2007)CrossRefGoogle Scholar
  9. 9.
    Sennoussaoui, M., Kenny, P., Brummer, N., de Villiers, E., Dumouchel, P.: Mixture of PLDA Models in I-Vector Space for Gender-Independent Speaker Recognition. In: Proc. Interspeech 2011, Florence, Italy (August 2011)Google Scholar
  10. 10.
    Campbell, W.M., Campbell, J.R., Reynolds, D.A., Singer, E., Torres-Carrasquillo, P.A.: Support Vector Machines for Speaker and Language Recognition. Computer Speech and Language 20, 210–229 (2006)CrossRefGoogle Scholar
  11. 11.
    Campbell, J.P.: Testing with the YOHO CD-ROM voice verification corpus. In: Proc. ICASSP (1995)Google Scholar
  12. 12.
    Yu, H., Han, J., Chang, K.C.C.: PEBL: Positive Example based Learning for Web Page Classification using SVM. In: Proc. of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 239–248. ACM (2002)Google Scholar
  13. 13.
    Lindell, Y., Pinkas, B.: An Efficient Protocol for Secure Two-Party Computation in the Presence of Malicious Adversaries. In: Naor, M. (ed.) EUROCRYPT 2007. LNCS, vol. 4515, pp. 52–78. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Kinnunena, T., Li, H.: An overview of text-independent speaker recognition: From features to supervectors. Speech Communication 52(1), 12–40 (2010)CrossRefGoogle Scholar
  15. 15.
    Smaragdis, P., Shashanka, M.: A Framework for Secure Speech Recognition. IEEE Transactions on Audio, Speech, and Language Processing 15(4), 1404–1413Google Scholar
  16. 16.
    Pathak, M., Rane, S., Sun, W., Raj, B.: Privacy Preserving Probabilistic Inference with Hidden Markov Models. In: Proc. ICASSP, Prague, Czech Republic (May 2011)Google Scholar
  17. 17.
    Prabhakar, S., Pankanti, S., Jain, A.K.: Biometric recognition: security and privacy concerns. IEEE Security & Privacy 1(2), 33–42Google Scholar
  18. 18.
    Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker Verification Using Adapted Gaussian Mixture Models. Digital Signal Processing 10(1-3), 19–41 (2000)CrossRefGoogle Scholar
  19. 19.
    Bimbot, F., Bonastre, J.-F., Fredouille, C., Gravier, G., Magrin-Chagnolleau, I., Meignier, S., Merlin, T., Ortega-García, J., Petrovska-Delacrétaz, D., Reynolds, D.A.: A Tutorial on Text-Independent Speaker Verification. EURASIP Journal on Advances in Signal Processing 4, 430–451 (2004)CrossRefGoogle Scholar
  20. 20.
    Reynolds, D.A.: Comparison of Background Normalization Methods for Text-Independent Speaker Verification. In: Proceedings of the European Conference on Speech Communication and Technology (September 1997)Google Scholar
  21. 21.
    Shou-Chun, Rose, R., Kenny, P.: Adaptive score normalization for progressive model adaptation in text independent speaker verification. In: Proc. ICASSP, Las Vegas, Nevada, USA (April 2008)Google Scholar
  22. 22.
    Paillier, P.: Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999)Google Scholar
  23. 23.
    Kershbaum, F., Biswas, D., de Hoogh, S.: Performance Comparison of Secure Comparison Protocols. In: Proceedings of the 2009 20th International Workshop on Database and Expert Systems Application, pp. 133–136 (2009)Google Scholar
  24. 24.
    Quisquater, J.-J., Guillou, L.C., Berson, T.A.: How to Explain Zero-Knowledge Protocols to Your Children. In: Brassard, G. (ed.) CRYPTO 1989. LNCS, vol. 435, pp. 628–631. Springer, Heidelberg (1990)Google Scholar
  25. 25.
    Gionis, A., Indyk, P., Motwani, R.: Similarity Search in High Dimensions via Hashing. In: Proceedings of the 25th Very Large Database (VLDB) Conference (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Manas Pathak
    • 1
  • Jose Portelo
    • 2
  • Bhiksha Raj
    • 1
  • Isabel Trancoso
    • 2
  1. 1.Carnegie Mellon UniversityPittsburghUSA
  2. 2.INESC-ID/ISTLisbonPortugal

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