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Toward Trustworthy Governance of AI-Generated Content (AIGC): A Blockchain-Driven Regulatory Framework for Secure Digital Ecosystems

  • Fan Yang
  • , Mohammad Zoynul Abedin
  • , Yanan Qiao
  • , Lvyang Ye
  • Xi'an Jiaotong University
  • Guangxi Normal University
  • Jilin University
  • Swansea University
  • Xi'an Institute of Electromechanical Information Technology
  • Science and Technology on Electromechanical Dynamic Control Laboratory

科研成果: 期刊稿件文章同行评审

31 引用 (Scopus)

摘要

Digital platforms are experiencing a growing presence of generative artificial intelligence (AI) content, raising concerns due to the prevalence of misinformation that disrupts market integrity. Consequently, the development of effective regulatory measures for overseeing generative AI content becomes imperative. This necessitates the establishment of mechanisms to detect and filter out inaccuracies, ensuring compliance with regulatory requirements. In addition, collaboration among experts, regulators, and AI developers is essential to encourage responsible AI deployment on digital platforms. Successful governance hinges on principles of transparency, accountability, and proactive risk management to navigate the evolving generative AI on digital platforms. Therefore, in order to address the security issues currently faced by artificial intelligence generated content (AIGC), this article first proposes a method of efficient cache mechanism for AIGC content. The secure method of determining the identity of AIGC content owners is proposed based on blockchain technology. Subsequently, it suggests mechanisms for access control and data encryption for generated content within a blockchain environment. Finally, it presents an efficient data supervision mechanism tailored to the AIGC environment. The methods outlined in this article aim to enhance security from three perspectives: protection of content creators' identities, safeguarding data security, and ensuring effective data supervision within the AIGC framework. The experimental results further confirm that our proposed method not only ensures the security of the AIGC framework but also provides an efficient data analysis and supervision solution for digital platforms.

源语言英语
页(从-至)14945-14962
页数18
期刊IEEE Transactions on Engineering Management
71
DOI
出版状态已出版 - 2024

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