Combining Cryptography with EEG Biometrics
Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometric...
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| Published in | Computational intelligence and neuroscience Vol. 2018; no. 2018; pp. 1 - 11 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2018
Hindawi John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024. |
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| AbstractList | Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024. Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024. |
| Audience | Academic |
| Author | Damaševičius, Robertas Woźniak, Marcin Kazanavičius, Egidijus Maskeliūnas, Rytis |
| AuthorAffiliation | 1 Department of Software Engineering, Kaunas University of Technology, Studentų 50-415, Kaunas, Lithuania 3 Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland 2 Centre of Real Time Computer Systems, Kaunas University of Technology, K. Baršausko 59-A338, Kaunas, Lithuania |
| AuthorAffiliation_xml | – name: 1 Department of Software Engineering, Kaunas University of Technology, Studentų 50-415, Kaunas, Lithuania – name: 3 Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland – name: 2 Centre of Real Time Computer Systems, Kaunas University of Technology, K. Baršausko 59-A338, Kaunas, Lithuania |
| Author_xml | – sequence: 1 fullname: Damasevicius, Robertas – sequence: 2 fullname: Maskeliunas, Rytis – sequence: 3 fullname: Kazanavicius, Egidijus – sequence: 4 fullname: Wozniak, Marcin |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29951089$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Copyright © 2018 Robertas Damaševičius et al. COPYRIGHT 2018 John Wiley & Sons, Inc. Copyright © 2018 Robertas Damaševičius et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2018 Robertas Damaševičius et al. 2018 |
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| SubjectTerms | Accuracy Authentication Biometric identification Biometrics Biometry Brain research Cryptography Cybersecurity Data security EEG Electrodes Electroencephalography International conferences Methods Physiology Privacy Research methodology Researchers Security systems Signal processing |
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| Title | Combining Cryptography with EEG Biometrics |
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