EEG-based cryptographic key and true random number generation for blockchain and bitcoin security

  • Dang Nguyen

    Student thesis: Doctoral Thesis


    Cryptography is used to protect sensitive and classified information in technology
    and science areas such as cryptocurrency Bitcoin and cryptography assurance
    in Blockchain. Bitcoin is the first decentralized cryptocurrency that had attracted
    a rapid growth both in value and popularity since its first release in late 2008. Its
    security is based on cryptographic protocols that allow users to perform secure online
    transactions. These protocols include digital signatures and encryption/decryption
    algorithms based on elliptic curves. The security of Bitcoin transactions is mainly
    relied on a random binary sequence generated from a random number generator and
    a private key to produce digital signatures that are used to validate the authenticity
    and integrity of Bitcoins. However, Bitcoin is a relatively new technology and has
    security challenges related to current key generation algorithms and random number
    generators. Recently, biometrics have been investigated for use in cryptographic
    applications such as key generation for encryption and decryption algorithms. In
    general, people can use their biometrics (including voice, face, iris, and fingerprint)
    as good alternatives, or supplements, to PINs and passwords for generating keys. In
    recent years, the brainwave signal has been investigated for person identification and
    authentication because of its benefits over traditional means, and it has been shown
    that the brainwave signal can be a good alternative for these cryptographic applications.
    However, little work has been done on the combination of cryptography and
    brainwave signals.
    This research project focuses on the use of Electroencephalography (EEG) as
    a new biometric to build EEG-based cryptosystems. These cryptosystems include
    EEG-based cryptographic key generation systems using given EEG signals with an
    authentication mechanism to generate keys that can be applied in the protection
    of Bitcoin wallets. The key generation systems are based on the quasi-stationary
    characteristics of the EEG that is used for person identification and authentication.
    This research also covers EEG-based random number generators based on EEG nonlinear
    and chaotic characteristics to enhance the security of digital signatures used in
    Bitcoin transactions. In addition, this research investigates the impacts of epilepsy,
    alcohol, and emotion characteristics on these systems, matters that have not been
    investigated and evaluated in previous studies.
    Evaluation experiments performed on the Australian EEG, Alcoholism, GrazIIIa,
    GrazA 2008 and DEAP datasets show better results for most of the proposed
    techniques than current traditional techniques to have lower values of false acceptance
    rates and false rejection rates. The experimental results also achieve higher key
    length generated, a factor that is crucial in practical security applications to make
    revealation of the keys more difficult. The experimental results have also shown
    that epilepsy, alcohol and emotion characteristics have impacts on the EEG-based
    key generation systems, but they do not affect the length and the randomess of the
    generated key. Moreover, these characteristics do not affect on EEG-based random
    number generators.
    Date of Award2019
    Original languageEnglish
    SupervisorDat Tran (Supervisor) & Dharmendra Sharma AM PhD (Supervisor)

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