An Efficiently Traceable Secure Data Sharing Scheme with Intelligence for Cloud Computing

Abstract

The growing demand for large models in industrial Internet necessitates secure sharing of cloud-stored training data. However, existing Ciphertext-Policy Attribute-Based Encryption schemes suffer from low efficiency and insufficient accuracy in tracing key-leaking users. We propose a traceable secure data sharing scheme based on credibility, which enforces dual access control via credibility and attributes. It strengthens the binding of user and key and integrates a large model to create an intelligent traceability framework, utilizing multi-layer query mechanisms and Merkle trees to reduce redundant verification operations and improve tracing efficiency. Under the DBDH assumption, the scheme is IND-CPA secure. Experiments on the “Insider Threat Test Dataset” demonstrate a traceability accuracy of 99.46%, significantly reducing unnecessary verification operations. The proposed scheme provides an efficient, accurate, and practical solution for secure data sharing and malicious user traceability in distributed industrial cloud environments.