Abstract
Biometric recognition has the potential to authenticate individuals by an intrinsic link between the individual and their physical, physiological and/or behavioral characteristics. This leads a higher security level than the authentication solely based on knowledge or possession. One of the reasons why biometrics is not completely accepted is the lack of trust in the storage of biometric templates in external servers. Biometric data are sensitive data which should be protected as is contemplated in the data protection regulation of many countries. In this work, we propose the use of biometric Learning Parity With Noise (LPN) commitments as template protection scheme. To the best of our knowledge, this is the first proposal for biometric template protection based on the LPN problem (that is, the difficulty of decoding random linear codes), which offers post-quantum security. Biometric features are compared in the protected domain. Irreversibility, revocability, and unlinkability properties are satisfied as well as resistance to False Acceptance Rate (FAR), cross-matching, Stolen Token, and similarity-based attacks. A recognition accuracy with a 0% FAR is achieved, because user-specific secret keys are employed, and the False Rejection Ratio (FRR) can be adjusted depending on a threshold to preserve the accuracy of the unprotected scheme in the Stolen Token scenario. A good performance in terms of execution time, template storage and operation complexity is obtained for security levels at least of 80 bits. The proposed scheme is employed in a dual-factor authentication protocol from the literature to illustrate how it provides security using authentication and database (cloud) servers that can be malicious. The proposed LPN-based protected scheme can be applied to any biometric trait represented by binary features and any matching score based on Hamming or Jaccard distances. In particular, experimental results are included of a practical finger vein-based recognition system implemented in Matlab.
Highlights
Nowadays, our society has accepted extensively the use of biometric systems as a way of user authentication
In this work, we have proposed the use of Learning Parity With Noise (LPN) commitments to construct a biometric template protection scheme
Irreversibility is based on the LPN problem, which is the difficulty of decoding random linear codes
Summary
Our society has accepted extensively the use of biometric systems as a way of user authentication. Our proposal of biometric LPN commitments uses a public generator matrix to convert biometric data to linear codewords that are randomized with a userspecific secret. The first biometric template protection scheme based on LPN commitments, whose hardness is a NP complete problem to classical and quantum computers. Irreversibility, revocability, unlinkability, and resistance to attacks are proven and compared to other proposals of biometric template protection schemes applied to finger veins. An LPN commitment is based on encoding a message (in our proposal, biometric data) by using a random linear code with some noise added to the codeword. Since the decoding problem in random linear codes is known to be robust for quantum as well as for classical computers, the search LPN problem is suitable for the construction of quantum-resistant commitments of secret biometric data B. The impostor is directly rejected without proceeding to a score measurement
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