Abstract
The growing popularity of cloud platforms store and process large-scale social network data, if we do not pay attention to the method of using a cloud platform, privacy leakage will become a serious problem. In this paper, we propose a distributed k-automorphism algorithm and a distributed subgraph matching method, the distributed k-automorphism algorithm can efficiently protect the privacy of the social networks in the cloud platform by adding noise edges to ensure k-automorphism and the distributed subgraph matching method can quickly obtain temp subgraph matching results. After temp results are joined, we can obtain correct results by recovering and filtering temp results according to the symmetry of the k-automorphism graph and k-automorphism functions in the client. We also propose a modified method that utilizing incremental thought to solve the problem of dynamic subgraph matching. The experiments show that the above methods are effective in dealing with large scale social network graph problem and these methods can effectively solve the problem of privacy leakage of subgraph matching.
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Acknowledgments
This work is partially supported by Natural Science Foundation of China (No.61562065). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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Zhang, XL., Yuan, Hc., Li, Zl., Zhang, Hx., Li, J. (2019). Distributed Subgraph Matching Privacy Preserving Method for Dynamic Social Network. In: Jin, H., Lin, X., Cheng, X., Shi, X., Xiao, N., Huang, Y. (eds) Big Data. BigData 2019. Communications in Computer and Information Science, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-1899-7_12
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DOI: https://doi.org/10.1007/978-981-15-1899-7_12
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