Research on Data Security Isolation and Sharing Mechanism in Cloud Multi-tenant Environment Based on Blockchain and Federated Learning
Year: 2025
Type: article
Abstract: In cloud computing multi-tenant environments, the contradiction between data security isolation and efficient sharing has become increasingly prominent. This paper proposes an innovative framework integrating blockchain technology and federated learning to address the imbalance between security isolation strength and data circulation efficiency in traditional technologies. Through dynamic encryption strategies, adaptive access control algorithms, and cross-tenant joint modeling methods, an integrated design of "secure isolation-intelligent sharing-trustworthy auditing" is achieved. A multi-dimensional permission management model based on attribute graphs is proposed, which dynamically optimizes access policies using machine learning. The tamper-proof feature of blockchain is leveraged to record the full lifecycle of data operations, enhancing the transparency of sharing processes. Federated learning technology is employed to enable secure joint analysis with the principle of "data in place, models moving." Experiments in a simulated medical cloud environment show that the proposed mechanism reduces data leakage risks by 40%, improves sharing efficiency by 30%, and increases the accuracy of federated learning models by 15% compared with traditional solutions, providing a solution with both theoretical innovation and engineering value for data security governance in multi-tenant environments.
DOI: 10.54097/4ypqxn17
Authors: Guangwei Li, Wenjing Fang, Kaifu Mao
Institution: Shandong Yingcai University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414464756
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