@inproceedings{2babe61ae6e047a5a9426cd58ccdd899,
title = "On the Study of EEG-based Cryptographic Key Generation",
abstract = "Biometric-based cryptographic key generation is regarded as a data mining approach that uses knowledge discovery techniques to extract biometric information to generate cryptographic keys for protecting secured data by encryption. This application has been widely used in security systems to limit the weakness of passwords. Although conventional biometrics such as fingerprint, face, voice, and handwriting contain biometric information that is unique and repeatable for each individual, they are difficult to change to be used in different purposes. In this paper, we propose a system to exploit human electroencephalography (EEG) data as a new biometric for cryptographic key generation. This system provides high potential because EEG is impossible to be faked or compromised. Our method is evaluated using the EEG Alcoholism and GrazIIIa datasets, and is shown to reliably produce secure cryptographic keys with a 99% success rate.",
keywords = "Cryptographic key, Data mining, EEG, Knowledge discovery",
author = "Dang Nguyen and Dat Tran and Dharmendra Sharma and Wanli Ma",
year = "2017",
month = sep,
day = "1",
doi = "10.1016/j.procs.2017.08.126",
language = "English",
volume = "112",
series = "Procedia Computer Science",
publisher = "Elsevier",
pages = "936--945",
booktitle = "Procedia Computer Science",
address = "Netherlands",
note = "21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, KES 2017 ; Conference date: 06-09-2017 Through 08-09-2017",
}