A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof Protocol
Biometric authentication is, over time, becoming an indispensable complementary component to traditional authentication methods that use passwords and tokens. As a result, the research interest in the protection techniques for the biometric template has also grown considerably. In this paper, we pre...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of the Computer Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9663008/ |
| _version_ | 1828058254504099840 |
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| author | Quang Nhat Tran Benjamin Peter Turnbull Min Wang Jiankun Hu |
| author_facet | Quang Nhat Tran Benjamin Peter Turnbull Min Wang Jiankun Hu |
| author_sort | Quang Nhat Tran |
| collection | DOAJ |
| description | Biometric authentication is, over time, becoming an indispensable complementary component to traditional authentication methods that use passwords and tokens. As a result, the research interest in the protection techniques for the biometric template has also grown considerably. In this paper, we present a light-weight AI-based biometric authentication that operates based on the binary representation of a biometric instance. In details, a binary classifier will be trained using the binary strings that represent the intraclass and interclass biometric subjects. The Support Vector Machine and Multi-layer Perceptron Neural Network are chosen as the classifier to evaluate the fingerprint-based and iris-based authentication capability. Afterward, the authenticated biometric string is fed to a hash function to produce a hash value, which is to be used in a Zero-Knowledge-Proof Protocol for the purpose of privacy preservation. In order to improve the recognition of the classifier, we devise a simple yet efficient strategy to enhance the discriminativeness of the binary strings and name it the Composite Features Retrieval. We evaluated the proposed method with the four publicly available fingerprint datasets FVC2002-DB1, FVC2002-DB2, FVC2002-DB3, and FVC2004-DB2 and the iris dataset UBIRISv1. The promising performance shows this method's capability. |
| first_indexed | 2024-04-10T21:25:29Z |
| format | Article |
| id | doaj.art-d64cd1936d3f402186fe0e01d9b5ac9f |
| institution | Directory Open Access Journal |
| issn | 2644-1268 |
| language | English |
| last_indexed | 2024-04-10T21:25:29Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Computer Society |
| spelling | doaj.art-d64cd1936d3f402186fe0e01d9b5ac9f2023-01-20T00:00:27ZengIEEEIEEE Open Journal of the Computer Society2644-12682022-01-01311010.1109/OJCS.2021.31383329663008A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof ProtocolQuang Nhat Tran0Benjamin Peter Turnbull1https://orcid.org/0000-0003-0440-5032Min Wang2https://orcid.org/0000-0002-1580-6387Jiankun Hu3https://orcid.org/0000-0003-0230-1432The University of New South Wales, Canberra, ACT, AustraliaThe University of New South Wales, Canberra, ACT, AustraliaThe University of New South Wales, Canberra, ACT, AustraliaThe University of New South Wales, Canberra, ACT, AustraliaBiometric authentication is, over time, becoming an indispensable complementary component to traditional authentication methods that use passwords and tokens. As a result, the research interest in the protection techniques for the biometric template has also grown considerably. In this paper, we present a light-weight AI-based biometric authentication that operates based on the binary representation of a biometric instance. In details, a binary classifier will be trained using the binary strings that represent the intraclass and interclass biometric subjects. The Support Vector Machine and Multi-layer Perceptron Neural Network are chosen as the classifier to evaluate the fingerprint-based and iris-based authentication capability. Afterward, the authenticated biometric string is fed to a hash function to produce a hash value, which is to be used in a Zero-Knowledge-Proof Protocol for the purpose of privacy preservation. In order to improve the recognition of the classifier, we devise a simple yet efficient strategy to enhance the discriminativeness of the binary strings and name it the Composite Features Retrieval. We evaluated the proposed method with the four publicly available fingerprint datasets FVC2002-DB1, FVC2002-DB2, FVC2002-DB3, and FVC2004-DB2 and the iris dataset UBIRISv1. The promising performance shows this method's capability.https://ieeexplore.ieee.org/document/9663008/Biometricsmultilayer perceptronneural networksupport vector machinebinary |
| spellingShingle | Quang Nhat Tran Benjamin Peter Turnbull Min Wang Jiankun Hu A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof Protocol IEEE Open Journal of the Computer Society Biometrics multilayer perceptron neural network support vector machine binary |
| title | A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof Protocol |
| title_full | A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof Protocol |
| title_fullStr | A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof Protocol |
| title_full_unstemmed | A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof Protocol |
| title_short | A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof Protocol |
| title_sort | privacy preserving biometric authentication system with binary classification in a zero knowledge proof protocol |
| topic | Biometrics multilayer perceptron neural network support vector machine binary |
| url | https://ieeexplore.ieee.org/document/9663008/ |
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