Securing AI Models with Blockchain-Based Provenance Tracking
Abstract
The security of AI models is increasingly threatened by adversarial attacks and unauthorized modifications. This paper presents a blockchain-based provenance tracking system designed to secure AI models throughout their lifecycle. By recording each step of the model development and deployment process on a blockchain, we create an immutable audit trail that can be used to verify the model's integrity and authenticity. The system also includes smart contracts that enforce access control policies, ensuring that only authorized users can modify or deploy AI models. We evaluate the effectiveness of the proposed system in various scenarios, including model training, transfer learning, and deployment in edge environments. Our results show that blockchain-based provenance tracking not only enhances the security of AI models but also facilitates compliance with regulatory requirements, such as GDPR and HIPAA, by providing transparent and auditable records of AI model usage.
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References
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