A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

Biometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users’ biometric traits to be permanently unavailable in the secure authentication system. To enh...

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Main Authors: Yazhou Wang, Bing Li, Yan Zhang, Jiaxin Wu, Qianya Ma
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/11/18/8497
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spelling doaj-c4cd51f2235a4d6883470254a51b91d82021-09-25T23:40:17ZengMDPI AGApplied Sciences2076-34172021-09-01118497849710.3390/app11188497A Secure Biometric Key Generation Mechanism via Deep Learning and Its ApplicationYazhou Wang0Bing Li1Yan Zhang2Jiaxin Wu3Qianya Ma4School of Microelectronics, Southeast University, Nanjing 210096, ChinaSchool of Microelectronics, Southeast University, Nanjing 210096, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing 210096, ChinaSchool of Microelectronics, Southeast University, Nanjing 210096, ChinaSchool of Microelectronics, Southeast University, Nanjing 210096, ChinaBiometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users’ biometric traits to be permanently unavailable in the secure authentication system. To enhance its security and privacy, we propose a secure biometric key generation method based on deep learning in this paper. Firstly, to prevent the information leakage of biometric data, we utilize random binary codes to represent biometric data and adopt a deep learning model to establish the relationship between biometric data and random binary code for each user. Secondly, to protect the privacy and guarantee the revocability of the biometric key, we add a random permutation operation to shuffle the elements of binary code and update a new biometric key. Thirdly, to further enhance the reliability and security of the biometric key, we construct a fuzzy commitment module to generate the helper data without revealing any biometric information during enrollment. Three benchmark datasets including ORL, Extended YaleB, and CMU-PIE are used for evaluation. The experiment results show our scheme achieves a genuine accept rate (GAR) higher than the state-of-the-art methods at a 1% false accept rate (FAR), and meanwhile satisfies the properties of revocability and randomness of biometric keys. The security analyses show that our model can effectively resist information leakage, cross-matching, and other attacks. Moreover, the proposed model is applied to a data encryption scenario in our local computer, which takes less than 0.5 s to complete the whole encryption and decryption at different key lengths.https://www.mdpi.com/2076-3417/11/18/8497biometricssecurityprivacydeep learning
collection DOAJ
language English
format Article
sources DOAJ
author Yazhou Wang
Bing Li
Yan Zhang
Jiaxin Wu
Qianya Ma
spellingShingle Yazhou Wang
Bing Li
Yan Zhang
Jiaxin Wu
Qianya Ma
A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application
Applied Sciences
biometrics
security
privacy
deep learning
author_facet Yazhou Wang
Bing Li
Yan Zhang
Jiaxin Wu
Qianya Ma
author_sort Yazhou Wang
title A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application
title_short A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application
title_full A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application
title_fullStr A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application
title_full_unstemmed A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application
title_sort secure biometric key generation mechanism via deep learning and its application
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2021-09-01
description Biometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users’ biometric traits to be permanently unavailable in the secure authentication system. To enhance its security and privacy, we propose a secure biometric key generation method based on deep learning in this paper. Firstly, to prevent the information leakage of biometric data, we utilize random binary codes to represent biometric data and adopt a deep learning model to establish the relationship between biometric data and random binary code for each user. Secondly, to protect the privacy and guarantee the revocability of the biometric key, we add a random permutation operation to shuffle the elements of binary code and update a new biometric key. Thirdly, to further enhance the reliability and security of the biometric key, we construct a fuzzy commitment module to generate the helper data without revealing any biometric information during enrollment. Three benchmark datasets including ORL, Extended YaleB, and CMU-PIE are used for evaluation. The experiment results show our scheme achieves a genuine accept rate (GAR) higher than the state-of-the-art methods at a 1% false accept rate (FAR), and meanwhile satisfies the properties of revocability and randomness of biometric keys. The security analyses show that our model can effectively resist information leakage, cross-matching, and other attacks. Moreover, the proposed model is applied to a data encryption scenario in our local computer, which takes less than 0.5 s to complete the whole encryption and decryption at different key lengths.
topic biometrics
security
privacy
deep learning
url https://www.mdpi.com/2076-3417/11/18/8497
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