Efficient Biometric Verification in Encrypted Domain

  • Maneesh Upmanyu
  • Anoop M. Namboodiri
  • K. Srinathan
  • C. V. Jawahar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Biometric authentication over public networks leads to a variety of privacy issues that needs to be addressed before it can become popular. The primary concerns are that the biometrics might reveal more information than the identity itself, as well as provide the ability to track users over an extended period of time. In this paper, we propose an authentication protocol that alleviates these concerns. The protocol takes care of user privacy, template protection and trust issues in biometric authentication systems. The protocol uses asymmetric encryption, and captures the advantages of biometric authentication. The protocol provides non-repudiable identity verification, while not revealing any additional information about the user to the server or vice versa. We show that the protocol is secure under various attacks. Experimental results indicate that the overall method is efficient to be used in practical scenarios.


Support Vector Machine Smart Card Authentication Protocol Homomorphic Encryption Biometric Authentication 
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.


  1. 1.
    Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 4–20 (2004)Google Scholar
  2. 2.
    Ratha, N.K., Connell, J.H., Bolle, R.M.: Enhancing security and privacy in biometrics-based authentication systems. IBM Systems Journal 40(3), 614–634 (2001)Google Scholar
  3. 3.
    Fontaine, C., Galand, F.: A survey of homomorphic encryption for nonspecialists. EURASIP J. Inf. Secur. 2007(1), 1–15 (2007)Google Scholar
  4. 4.
    Jain, A.K., Nandakumar, K., Nagar, A.: Biometric template security. EURASIP J. Adv. Signal Process. 8(2), 1–17 (2008)Google Scholar
  5. 5.
    Uludag, U., Pankanti, S., Prabhakar, S., Jain, A.K.: Biometric cryptosystems: Issues and challenges. Proceedings of the IEEE 92(6), 948–960 (2004)Google Scholar
  6. 6.
    Ratha, N., Chikkerur, S., Connell, J., Bolle, R.: Generating cancelable fingerprint templates. IEEE Trans. on PAMI 29(4), 561–572 (2007)Google Scholar
  7. 7.
    Teoh, A., Jin, B., Connie, T., Ngo, D., Ling, C.: Remarks on BioHash and its mathematical foundation. Information Processing Letters 100(4), 145–150 (2006)Google Scholar
  8. 8.
    Kong, A., Cheung, K., Zhang, D., Kamel, M., You, J.: An analysis of biohashing and its variants. Pattern Recognition 39(7), 1359–1368 (2006)Google Scholar
  9. 9.
    Juels, A., Sudan, M.: A fuzzy vault scheme. DCC 38(2), 237–257 (2006)Google Scholar
  10. 10.
    Dodis, Y., Reyzin, L., Smith, A.: Fuzzy extractors: How to generate strong keys from biometrics and other noisy data. In: Cachin, C., Camenisch, J.L. (eds.) EUROCRYPT 2004. LNCS, vol. 3027, pp. 523–540. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Walter, J.S., Boult, T.E.: Cracking fuzzy vaults and biometric encryption. In: Biometrics Symposium (2007)Google Scholar
  12. 12.
    Nagai, K., Kikuchi, H., Ogata, W., Nishigaki, M.: ZeroBio: Evaluation and development of asymmetric fingerprint authentication system using oblivious neural network evaluation protocol. In: Proceedings of ARES 2007, pp. 1155–1159 (2007)Google Scholar
  13. 13.
    Farooq, F., Bolle, R.M., Jea, T.Y., Ratha, N.: Anonymous and revocable fingerprint recognition. In: Proceedings of the Biometrics Worshop (CVPR 2007), pp. 1–7 (2007)Google Scholar
  14. 14.
    Menezes, A., van, O., Paul, C., Vanstone, S.A.: Handbook of Applied Cryptography (1996)Google Scholar
  15. 15.
    Abe, S.: Support Vector Machines For Pattern Classification. Springer, Heidelberg (2005)Google Scholar
  16. 16.
    Jain, A.K., Ross, A., Pankanti, S.: A prototype hand geometry-based verification system. In: Proceedings of the AVBPA 1999, pp. 166–171 (1999)Google Scholar
  17. 17.
    Wang, Y., Han, J.: Iris recognition using SVM. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 622–628. Springer, Heidelberg (2004)Google Scholar
  18. 18.
    Blum, T., Paar, C.: High-radix montgomery modular exponentiation on reconfigurable hardware. IEEE Transactions on Computers 50(7), 759–764 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Maneesh Upmanyu
    • 1
  • Anoop M. Namboodiri
    • 1
  • K. Srinathan
    • 1
  • C. V. Jawahar
    • 1
  1. 1.Center for Visual Information TechnologyInternational Institute of Information TechnologyHyderabadIndia

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