Journal
COMPUTERS & SECURITY
Volume 118, Issue -, Pages -Publisher
ELSEVIER ADVANCED TECHNOLOGY
DOI: 10.1016/j.cose.2022.102750
Keywords
Relation-based attacks; Privacy preserving; Biometrics hashing; Conditional min-entropy; Deep neural network
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Funding
- National Natural Science Foundation of Guangdong [2021A1515012020, 2017A030312008]
- Guangzhou science and technology plan project [202002030298]
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Biometric hashing is widely used in privacy-preserving biometric recognition systems due to its irreversibility, low computational cost, and high storage efficiency. However, the security of biometric hashing has been challenged by relation-based attacks. To address this issue, researchers propose a Secure Biometric Hashing scheme against Relation-Based Attacks (SBH-RA), which minimizes the leakage of distance relation on the original biometric by maximizing the conditional min-entropy of the signs of inter-class distance differences.
With its irreversibility, low computational cost, and high storage efficiency, biometric hashing is widely used in privacy-preserving biometric recognition systems, yet its security has been recently challenged due to the emergence of relation-based attacks (RA). To address this issue, we model and analyse the RA, and discover that maximizing the conditional min-entropy of the signs of inter-class distance differences in the original space and hash space can minimize the leakage of the distance relation on the original biometric. Consequently, we develop a Secure Biometric Hashing scheme against Relation-Based Attacks (SBH-RA). SBH-RA maximizes the conditional min-entropy by using the cosine function as a distance mapping function. Meanwhile, it learns hash codes by a classification loss and quantization loss to ensure the accuracy of recognition. Our study demonstrates that SBH-RA not only offers higher security but also yields comparable or even superior recognition performance over existing biometric hashing methods experimentally and theoretically. Given 1024 bits hash codes from SBH-RA, it brings a decrease of 21% in Equal Error Rate on face dataset LFW compared with widely used Biohashing. Besides, even under white-box attacks, the probability of a successful attack is smaller than 1.69 x 2(-346). (C) 2022 Elsevier Ltd. All rights reserved.
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