Achieving Graph Clustering Privacy Preservation Based on Structure Entropy in Social IoT

Decoding the real structure from the Social Internet-of-Things (SIoT) network with a large-scale noise structure plays a fundamental role in data mining. Protecting private information from leakage in the mining process and obtaining accurate mining results is a significant challenge. To tackle this issue, we present a graph clustering privacy-preserving method based on structure entropy, which combines data mining with the structural information theory. Specially, user private information in SIoT is encrypted by Brakerski-Gentry-Vaikuntanathan (BGV) homomorphism to generate a graph structure in the ciphertext state, the ciphertext graph structure is then divided into different modules by applying a 2-D structural information solution algorithm and a entropy reduction principle node module partition algorithm, and the K-dimensional structural information solution algorithm is utilized to further cluster the internal nodes of the partition module. Moreover, normalized structural information and network node partition similarity are introduced to analyze the correctness and similarity degree of clustering results. Finally, security analysis and theoretical analysis indicate that this scheme not only guarantees the correctness of the clustering results but also improves the security of private information in SIoT. Experimental evaluation and analysis shows that the clustering results of this scheme have higher efficiency and reliability.

Automated summary

This paper presents a graph clustering privacy-preserving method based on structure entropy, which protects private information and ensures accurate mining results. Experimental evaluation shows that this method has higher efficiency and reliability.