On the Study of EEG-based Cryptographic Key Generation

Research output: A Conference proceeding or a Chapter in BookConference contribution

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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.

Original languageEnglish
Title of host publicationProcedia Computer Science
Subtitle of host publicationInternational Conference on Kno wledge Based and Intelligent Information and Engineering Systems (KES2017)
Place of PublicationMarseille, France
PublisherElsevier
Pages936-945
Number of pages10
Volume112
DOIs
Publication statusPublished - 1 Sep 2017
Event21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, KES 2017 - Marseille, France
Duration: 6 Sep 20178 Sep 2017

Publication series

NameProcedia Computer Science
PublisherElsevier BV
ISSN (Print)1877-0509

Conference

Conference21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, KES 2017
CountryFrance
CityMarseille
Period6/09/178/09/17

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Nguyen, D., Tran, D., Sharma, D., & Ma, W. (2017). On the Study of EEG-based Cryptographic Key Generation. In Procedia Computer Science: International Conference on Kno wledge Based and Intelligent Information and Engineering Systems (KES2017) (Vol. 112, pp. 936-945). (Procedia Computer Science). Marseille, France: Elsevier. https://doi.org/10.1016/j.procs.2017.08.126