A Cross-Domain Data Flow Security Governance and Privacy Protection Mechanism for Distributed Digital Art Platforms

INTRODUCTION: Distributed digital art platforms face ownership ambiguity, privacy leakage, unauthorized access, and audit difficulties in cross-domain data flows. OBJECTIVES: To enhance trust, privacy, controllability, and auditability through a dedicated data security governance mechanism. METHODS: A closed-loop governance mechanism jointly models art data, AIGC models, copyrights, and transactions, integrating five coordinated modules: damage-tolerant ownership verification, privacy-preserving cross-chain auditing, dynamic access control, secure delivery with rollback, and efficient traceability. RESULTS: Ownership confirmation reaches 97.9%–98.7% (mild perturbations) and 85.9% (combined); authorization accuracy: 95.6%; illegal access interception: 94.9%; normal transaction audit pass rate: 99.3%; anomaly detection: up to 100%; on-chain storage compression: 99.99% (50 MB); traceability success: 94.8%. Access latency remains below 20 ms with 180+ transactions/s under concurrency.  CONCLUSION: The mechanism effectively secures cross-domain data flows with high trustworthiness and low overhead, suitable for distributed digital art ecosystems.