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Integrating blockchain technology with generative artificial intelligence (GAI) provides a transformative method for ensuring data provenance, model integrity, and trust in AI-generated content. Blockchain’s decentralized and immutable qualities improve transparency, security, and accountability in AI workflows, addressing issues related to data authenticity, model tampering, and ethical standards. This paper examines the intersection of blockchain and GAI, discussing major challenges such as computational overhead, scalability limits, regulatory restrictions, and energy efficiency concerns. It also offers potential solutions, including hybrid blockchain models, Layer 2 scaling techniques, privacy-preserving AI frameworks, and energy-efficient consensus mechanisms. By proposing a structured framework for blockchain-enabled GAI, this research emphasizes its potential to develop resilient and trustworthy AI ecosystems while tackling important technical and ethical issues.

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Blockchain Driven Generative AI: Ensuring Data Provenance and Model Integrity

  • 会议论文

  • 发表日期：

    2026年

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  • Mahfooz Alam,
  • Deepak,
  • Divya Mishra,
  • Mohammad Shahid,
  • Maria Lapina

摘要

Integrating blockchain technology with generative artificial intelligence (GAI) provides a transformative method for ensuring data provenance, model integrity, and trust in AI-generated content. Blockchain’s decentralized and immutable qualities improve transparency, security, and accountability in AI workflows, addressing issues related to data authenticity, model tampering, and ethical standards. This paper examines the intersection of blockchain and GAI, discussing major challenges such as computational overhead, scalability limits, regulatory restrictions, and energy efficiency concerns. It also offers potential solutions, including hybrid blockchain models, Layer 2 scaling techniques, privacy-preserving AI frameworks, and energy-efficient consensus mechanisms. By proposing a structured framework for blockchain-enabled GAI, this research emphasizes its potential to develop resilient and trustworthy AI ecosystems while tackling important technical and ethical issues.