Mitsubishi Electric Research Laboratories

Secure Biometrics

Current methods of using biometrics are often insecure since they store the biometric in the clear, compromising security and enabling identity theft. Our method obtains security by transforming the biometric into a syndrome (a compressed and scrambled bit stream that contains less information than the original biometric). Since only the syndrome and not the original biometric is stored, an attacker that learns the syndrome cannot determine the original biometric and therefore cannot impersonate the user.

Background & Objective:  Biometrics such as fingerprints, irises, and faces are increasingly prevalent in authentication, encryption and access control. Biometrics are slightly different each time they are measured. Therefore they cannot be stored in encrypted form as passwords are because the encrypted form of the original biometric and the encrypted form of a later measurement of the same biometric would not match. Consequently, most systems store biometrics in the clear. For biometrics to be broadly accepted, we need a way to store biometrics in a secure form that cannot be used by an attacker to impersonate a valid user. At the same time the authentication method needs to be robust to the natural measurement variation of the biometric.

Technical Discussion:  Our method obtains security by transforming the biometric into a binary vector which is then multiplied by the parity check matrix of a publicly known low density parity check code. The output is the biometric's syndrome, a compressed and scrambled version of the original biometric with two essential features. First, the syndrome contains less information than the original. If only the syndrome is stored, and not the original biometric itself, an attacker that learns the syndrome cannot recover the original biometric. Second, when the syndrome is combined with another measurement of the same biometric, it is possible to correct the measurement noise and exactly recover the original biometric through belief propagation decoding. The original biometric can therefore serve as a shared secret. The original biometric can be used, e.g., as a secret password or a cryptographic key, using standard techniques.

Publications:
Vetro, A.; Draper, S.; Rane, S.; Yedidia, J., "Securing Biometric Data", Distributed Source Coding, ISBN-13: 978-0-12-374485-2 Algorithms and Applications, January 2009 (Elsevier, TR2009-002)

Sutcu, Y.; Rane, S.; Yedidia, J.S.; Draper, S.C.; Vetro, A., "Feature Extraction for a Slepian-Wolf Biometric System Using LDPC Codes", IEEE International Symposium on Information Theory (ISIT), July 2008 (ISIT 2008, TR2008-036)

*   Sutcu, Y.; Rane, S.; Yedidia, J.S.; Draper, S.C.; Vetro, A., "Feature Transformation of Biometric Templates for Secure Biometric Systems based on Error Correcting Codes", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2008 (IEEE Xplore, TR2008-029)

Jain, A.K.; Chellappa, R.; Draper, S.C.; Memon, N.; Phillips, P.J.; Vetro, A., "Signal Processing for Biometric Systems (DSP Forum)", IEEE Signal Processing Magazine, ISSN: 1053-5888, Vol. 24, Issue 6, pp. 146-152, November 2007 (IEEE Xplore, TR2007-071)

Draper, S.; Martinian, E., "Compound Conditional Source Coding, Slepian-Wolf List Decoding, and Applications to Media Coding", IEEE International Symposium on Information Theory (ISIT), June 2007 (TR2007-023)

Draper, S.C.; Khisti, A.; Martinian, E.; Vetro, A.; Yedidia, J.S., "Using Distributed Source Coding to Secure Fingerprint Biometrics", IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ISSN: 1520-6149, Vol. 2, pp. II-129--II-132, April 2007 (IEEE Xplore, TR2007-005)

Draper, S.C.; Khisti, A.; Martinian, E.; Vetro, A.; Yedidia, J.S., "Secure Storage of Fingerprint Biometrics Using Slepian-Wolf Codes", Information Theory and Applications Workshop (ITA), January 2007 (ITA 2007, TR2007-006)

*   Martinian, E.; Yekhanin, S.; Yedidia, J.S., "Secure Biometrics Via Syndromes", Allerton Conference on Communications, Control and Computing, September 2005 (Allerton Conference on Communications, Control and Computing, TR2005-112)

Technology Areas:
Multimedia
Computer Vision
Imaging

Modification Date:  January 16, 2009