Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage

• URL: http://arxiv.org/abs/2404.06077v2
• Date: Mon, 07 Jul 2025 07:28:50 GMT
• Title: Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage
• Authors: Qin Wang, Guangsheng Yu, Yilin Sai, H. M. N. Dilum Bandara, Shiping Chen,
• Abstract summary: textscIBis is a blockchain-based framework tailored for AI model training.<n>textscIBis integrates on-chain registries for datasets, licenses and models, alongside off-chain signing services.<n>We implement textscIBis using Daml on the Canton blockchain.
• Score: 3.114654787133255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Artificial Intelligence (AI) integrates into diverse areas, particularly in content generation, ensuring rightful ownership and ethical use becomes paramount, AI service providers are expected to prioritize responsibly sourcing training data and obtaining licenses from data owners. However, existing studies primarily center on safeguarding static copyrights, which simply treat metadata/datasets as non-fungible items with transferable/trading capabilities, neglecting the dynamic nature of training procedures that can shape an ongoing trajectory. In this paper, we present \textsc{IBis}, a blockchain-based framework tailored for AI model training workflows. Our design can dynamically manage copyright compliance and data provenance in decentralized AI model training processes, ensuring that intellectual property rights are respected throughout iterative model enhancements and licensing updates. Technically, \textsc{IBis} integrates on-chain registries for datasets, licenses and models, alongside off-chain signing services to facilitate collaboration among multiple participants. Further, \textsc{IBis} provides APIs designed for seamless integration with existing contract management software, minimizing disruptions to established model training processes. We implement \textsc{IBis} using Daml on the Canton blockchain. Evaluation results showcase the feasibility and scalability of \textsc{IBis} across varying numbers of users, datasets, models, and licenses.

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