Abstract
Community detection is a trendy area in research on complex network analysis and has a wide range of real-world applications, like advertising. As people become increasingly concerned about privacy, protecting participants’ privacy in distributed community detection has become a new challenge. When applied to not identically and independently distributed (Non-IID) data, most federated graph algorithms suffer from the weight divergence problem caused by diversified training of local and global models, resulting in accuracy degradation. Furthermore, the privacy protection approaches based on anonymization, such as differential privacy (DP), and cryptography, such as homomorphic encryption (HE), incur accuracy loss and high time consumption, respectively. In this paper, we propose a globally consistent vertical federated graph autoencoder (GCVFGAE) algorithm, which builds a globally consistent model among the coordinator and all participants to solve the Non-IID graph data problem. As well, an attribute blinding strategy based on security aggregation is developed to protect the network privacy of each participant without losing accuracy. Both real-world and artificial networks’ experiments show that our algorithm reaches higher accuracy than the existing vertical federated graph neural networks (GNNs) and the simple distributed graph autoencoder without federated learning and detects communities identical to those found by the standard graph autoencoder (GAE).
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References
Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175–1191 (2017)
Chen, C., et al.: Vertically federated graph neural network for privacy-preserving node classification. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-2022, pp. 1959–1965. International Joint Conferences on Artificial Intelligence Organization (2022). https://doi.org/10.24963/ijcai.2022/272, main Track
Chu, S.C., Chen, L., Kumar, S., Kumari, S., Rodrigues, J.J., Chen, C.M.: Decentralized private information sharing protocol on social networks. Secur. Commun. Netw. 2020, 1–12 (2020)
Determann, L., Ruan, Z.J., Gao, T., Tam, J.: China’s draft personal information protection law. J. Data Prot. Priv. 4(3), 235–259 (2021)
Goddard, M.: The EU general data protection regulation (GDPR): European regulation that has a global impact. Int. J. Mark. Res. 59(6), 703 (2017)
Gonzalez, J.E., Low, Y., Gu, H., Bickson, D., Guestrin, C.: \(\{\)PowerGraph\(\}\): distributed \(\{\)Graph-Parallel\(\}\) computation on natural graphs. In: 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2012), pp. 17–30 (2012)
Huang, B., Wang, C., Wang, B.: NMLPA: uncovering overlapping communities in attributed networks via a multi-label propagation approach. Sensors 19(2), 260 (2019)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)
Kumar, S., Kumar, P.: Privacy preserving in online social networks using fuzzy rewiring. IEEE Trans. Eng. Manag. 70, 2071–2079 (2021)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)
Liu, R., Yu, H.: Federated graph neural networks: overview, techniques and challenges (2022)
Majeed, A., Lee, S.: Anonymization techniques for privacy preserving data publishing: a comprehensive survey. IEEE Access 9, 8512–8545 (2020)
Ni, X., Xu, X., Lyu, L., Meng, C., Wang, W.: A vertical federated learning framework for graph convolutional network. arXiv preprint arXiv:2106.11593 (2021)
Ji, T., Luo, C., Guo, Y., Wang, Q., Yu, L.: Community detection in online social networks: a differentially private and parsimonious approach. IEEE Trans. Comput. Soc. Syst. 7(1), 151–163 (2020)
Xie, H., Ma, J., Xiong, L., Yang, C.: Federated graph classification over non-iid graphs. Adv. Neural Inf. Process. Syst. 34, 18839–18852 (2021)
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019)
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No. 62002063 and No. U21A20472, the National Key Research and Development Plan of China under Grant No.2021YFB36-00503, the Fujian Collaborative Innovation Center for Big Data Applications in Governments, the Fujian Industry-Academy Cooperation Project under Grant No. 2017H6008 and No. 2018H6010, the Natural Science Foundation of Fujian Province under Grant No.2020J05112, the Fujian Provincial Department of Education under Grant No.JAT190026, the Major Science and Technology Project of Fujian Province under Grant No.2021HZ022007 and Haixi Government Big Data Application Cooperative Innovation Center.
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Fang, Y., Huang, Q., Ye, E., Guo, W., Guo, K., Chen, X. (2023). Globally Consistent Vertical Federated Graph Autoencoder for Privacy-Preserving Community Detection. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2022. Communications in Computer and Information Science, vol 1681. Springer, Singapore. https://doi.org/10.1007/978-981-99-2356-4_7
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DOI: https://doi.org/10.1007/978-981-99-2356-4_7
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