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Fuzzy Extractors for Biometric Identification

Conference Paper


Download full-text (Open Access)

Abstract


  • © 2017 IEEE. Fuzzy extractor provides key generation from biometrics and other noisy data. The generated key is seamlessly usable for any cryptographic applications because its information entropy is sufficient for security. Biometric authentication offers natural and passwordless user authentication in various systems where fuzzy extractors can be used for biometric information security. Typically, a biometric system operates in two modes: verification and identification. However, existing fuzzy extractors does not support efficient user identification. In this paper, we propose a succinct fuzzy extractor scheme which enables efficient biometric identification as well as verification that it satisfies the security requirements. We show that the proposed scheme can be easily used in both verification and identification modes. To the best of our knowledge, we propose the first fuzzy extractor based biometric identification protocol. The proposed protocol is able to identify a user with constant computational cost rather than linear-time computation required by other fuzzy extractor schemes. We also provide security analysis of proposed schemes to show their security levels. The implementation shows that the performance of proposed identification protocol is constant and it is close to that of verification protocols.

UOW Authors


  •   LI, Nan (external author)
  •   Guo, Fuchun
  •   Mu, Yi (external author)
  •   Susilo, Willy
  •   Nepal, Sanjay K. (external author)

Publication Date


  • 2017

Citation


  • LI, N., Guo, F., Mu, Y., Susilo, W. & Nepal, S. (2017). Fuzzy Extractors for Biometric Identification. 37th IEEE International Conference on Distributed Computing Systems (ICDCS 2017) (pp. 667-677). United States: IEEE.

Scopus Eid


  • 2-s2.0-85027278851

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1698&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/697

Start Page


  • 667

End Page


  • 677

Place Of Publication


  • United States

Abstract


  • © 2017 IEEE. Fuzzy extractor provides key generation from biometrics and other noisy data. The generated key is seamlessly usable for any cryptographic applications because its information entropy is sufficient for security. Biometric authentication offers natural and passwordless user authentication in various systems where fuzzy extractors can be used for biometric information security. Typically, a biometric system operates in two modes: verification and identification. However, existing fuzzy extractors does not support efficient user identification. In this paper, we propose a succinct fuzzy extractor scheme which enables efficient biometric identification as well as verification that it satisfies the security requirements. We show that the proposed scheme can be easily used in both verification and identification modes. To the best of our knowledge, we propose the first fuzzy extractor based biometric identification protocol. The proposed protocol is able to identify a user with constant computational cost rather than linear-time computation required by other fuzzy extractor schemes. We also provide security analysis of proposed schemes to show their security levels. The implementation shows that the performance of proposed identification protocol is constant and it is close to that of verification protocols.

UOW Authors


  •   LI, Nan (external author)
  •   Guo, Fuchun
  •   Mu, Yi (external author)
  •   Susilo, Willy
  •   Nepal, Sanjay K. (external author)

Publication Date


  • 2017

Citation


  • LI, N., Guo, F., Mu, Y., Susilo, W. & Nepal, S. (2017). Fuzzy Extractors for Biometric Identification. 37th IEEE International Conference on Distributed Computing Systems (ICDCS 2017) (pp. 667-677). United States: IEEE.

Scopus Eid


  • 2-s2.0-85027278851

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1698&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/697

Start Page


  • 667

End Page


  • 677

Place Of Publication


  • United States