Abstract
Community detection is an effective approach to unveiling relationships among individuals in social networks. Detecting communities without privacy leakage remains an area of ongoing and indispensable focus. Therefore, anonymization and differential privacy based community detection methods are proposed to protect the privacy of social network information. However, the above methods cause inevitable accuracy loss in some way, resulting in the low utility in the final community division. In this paper, we propose a secure and efficient interaction protocol based on homomorphic encryption to find the index of the maximum value of encrypted floating-point numbers. Besides, we design a novel federated community detection framework, using user-server interactions to adjust and construct global optimal community division results, which could not only get an effective community division model but also guarantee strong privacy preservation. Through theoretical analysis and empirical experiments, the time cost of our proposed secure protocol is \(4\times \) faster than previous works. Meanwhile, our framework ensures modularity error in the range of 0.03 comparing with the plaintext framework, and modularity improves at least 0.3 with 3 other state-of-the-art privacy-preserving community detection schemes.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 62072109, No. U1804263) and Natural Science Foundation of Fujian Province (No. 2021J06013).
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Zheng, Z., Chen, Z., Liu, X., Jiang, N. (2023). Federated Community Detection in Social Networks. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_8
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