Combining Cryptography with EEG Biometrics

• Published in: Computational intelligence and neuroscience Vol. 2018; no. 2018; pp. 1 - 11
• Main Authors: Damasevicius, Robertas, Maskeliunas, Rytis, Kazanavicius, Egidijus, Wozniak, Marcin
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc
• Subjects: 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
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2018/1867548

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.