Secure IoT Data Sharing via Semi-Supervised Clustering Federated Learning with Fuzzy Multi-Objective Decision-Making and SecureBoost Integration
Abstract
The Internet of Things enables digital transactions and data sharing, but poses significant security risks during data transmission. To address the issues of weak data sharing security and stability, this study proposes a data sharing technology for Internet of Things. The framework integrates semi-supervised clustering with fuzzy multi-objective decision making and SecureBoost encryption, evaluated on USPS and synthetic datasets. Experimental results show that the Semi-Supervised Clustering Federated Learning algorithm achieves 94.6% accuracy on synthetic datasets, outperforming Multi-View Deep Subspace Clustering Networks (90.3%) and Mid-level Deep Pattern Mining (84.6%). Furthermore, evaluation of the proposed fusion data sharing technology reveals that the key encryption time remains within 250 ms for files smaller than 10 MB. For a 1 MB file, the decryption time is 19 ms. These results demonstrate that the proposed technology prevents data leakage and enables secure multi-party transactions. This study contributes to future secure access to diverse resource data in Internet of Things and ensures fair data sharing.References
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