Research on AI Trusted Traceability Technology for Data Element Circulation

Abstract

This article studies AI trustworthy traceability technology for data element circulation. The system has sorted out key technologies such as blockchain certification, digital watermarking, federated learning (FL) auditing, and trusted execution environment (TEE), and compared and analyzed them from the dimensions of applicability, security, and scalability; Furthermore, a multi-layer fusion trustworthy traceability framework is proposed, which integrates mechanisms such as data identification, model watermarking, on chain authentication, and smart contracts, and constructs a four layer collaborative architecture covering the data layer, model layer, circulation layer, and application layer, achieving trustworthy traceability of data throughout its entire lifecycle from production, circulation to application. This framework establishes a digital identity for data through a dual identification of "data fingerprint+digital watermark", utilizes blockchain and smart contracts to achieve transparent and controllable circulation processes, relies on TEE to ensure trustworthy execution in the inference stage, and supports responsibility tracing through blood relationship graphs and audit logs. Finally, the application prospects of the framework in scenarios such as data transactions, model services, industry data sharing, and AIGC (AI Generated Content) governance were discussed, and open challenges such as performance, interoperability, and privacy traceability balance were pointed out, providing technical references for the trustworthy circulation of data elements.