A Secure and Privacy-Preserving E-Government Framework Using Blockchain and Artificial Immunity

Noe Elisa (Lead Author), Longzhi Yang, Fei Chao, Nitin Kumar Naik, Tossapon Boongoen

Research output: Contribution to journalArticlepeer-review

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Abstract

Electronic Government (e-Government) systems constantly provide greater services to people, businesses, organisations, and societies by offering more information, opportunities, and platforms with the support of advances in information and communications technologies. This usually results in increased system complexity and sensitivity, necessitating stricter security and privacy-protection measures. The majority of the existing e-Government systems are centralised, making them vulnerable to privacy and security threats, in addition to suffering from a single point of failure. This study proposes a decentralised e-Government framework with integrated threat detection features to address the aforementioned challenges. In particular, the privacy and security of the proposed e-Government system are realised by the encryption, validation, and immutable mechanisms provided by Blockchain. The insider and external threats associated with blockchain transactions are minimised by the employment of an artificial immune system, which effectively protects the integrity of the Blockchain. The proposed e-Government system was validated and evaluated by using the framework of Ethereum Visualisations of Interactive, Blockchain, Extended Simulations (i.e. eVIBES simulator) with two publicly available datasets. The experimental results show the efficacy of the proposed framework in that it can mitigate insider and external threats in e-Government systems whilst simultaneously preserving the privacy of information.

Original languageEnglish
Pages (from-to)8773-8789
Number of pages17
JournalIEEE Access
Volume11
Early online date25 Jan 2023
DOIs
Publication statusPublished - 30 Jan 2023
Externally publishedYes

Keywords

  • E-Government
  • artificial immune system
  • blockchain
  • insider threat
  • privacy-preserving

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