Distributed Subgraph Matching Privacy Preserving Method for Dynamic Social Network

  • Xiao-Lin ZhangEmail author
  • Hao-chen Yuan
  • Zhuo-lin Li
  • Huan-xiang Zhang
  • Jian Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1120)


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.


Cloud platform Subgraph matching Protecting privacy Distributed 



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.


  1. 1.
    Hay, M., Miklau, G., Jensen, D., et al.: Resisting structural re-identification in anonymized social networks. VLDB J. 19(6), 797–823 (2010)CrossRefGoogle Scholar
  2. 2.
    Chang, Z., Zou, L., Li, F.: Privacy preserving subgraph matching on large graphs in cloud. In: International Conference on Management of Data, pp. 199–213. ACM (2016)Google Scholar
  3. 3.
    He, H., Singh, A.K.: Query language and access methods for graph databases. In: Aggarwal, C., Wang, H. (eds.) Managing and Mining Graph Data, pp 125–160. Springer, Boston (2010). Scholar
  4. 4.
    Yan, X., Yu, P.S., Han, J.: Substructure similarity search in graph databases. In: ACM SIGMOD International Conference on Management of Data, Baltimore, Maryland, USA, June, pp. 766–777 (2005)Google Scholar
  5. 5.
    Yuan, Y., Wang, G., Chen, L., Wang, H.: Efficient subgraph similarity search on large probabilistic graph databases. Proc. VLDB Endowment 5(9), 800–811 (2012)CrossRefGoogle Scholar
  6. 6.
    Sun, Z., Wang, H., Wang, H., Shao, B., Li, J.: Efficient subgraph matching on billion node graphs. Proc. VLDB Endowment 5(9), 788–799 (2012)CrossRefGoogle Scholar
  7. 7.
    Zou, L., Chen, L.: k-automorphism: a general framework for privacy preserving network publication. Proc. VLDB Endowment 2, 946–957 (2009)CrossRefGoogle Scholar
  8. 8.
    Yuan, M., Chen, L., Yu, Philip S., Mei, H.: Privacy preserving graph publication in a distributed environment. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds.) APWeb 2013. LNCS, vol. 7808, pp. 75–87. Springer, Heidelberg (2013). Scholar
  9. 9.
    Tai, C.H., Yu, P.S., Yang, D. N., Chen, M.S.:. Privacy-preserving social network publication against friendship ttacks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 7, pp. 1262–1270. ACM (2011)Google Scholar
  10. 10.
    Cheng, J., Fu, W.C., Liu, J.: K-isomorphism: privacy preserving network publication against structural attacks. In: ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, June, vol. 4, pp. 459–470. DBLP (2010)Google Scholar
  11. 11.
    Bhagat, S., Cormode, G., Krishnamurthy, B., Srivastava, D.: Class-based graph anonymization for social network data. Proc. VLDB Endowment 2(1), 766–777 (2009)CrossRefGoogle Scholar
  12. 12.
    Campan, A., Traian, M.: A clustering approach for data and structural anonymity in social networks. In: Privacy, Security, and Trust in KDD Workshop, PinKDD, pp. 33–54 (2008)Google Scholar
  13. 13.
    Cormode, G., Srivastava, D., Yu, T., Zhang, Q.: Anonymizing bipartite graph data using safe groupings. VLDB J. 19(1), 115–139 (2010)CrossRefGoogle Scholar
  14. 14.
    Das, S., Egecioglu, O., El Abbadi, A.: Anonymizing weighted social network graphs. In: IEEE, International Conference on Data Engineering, vol. 41, pp. 904–907. IEEE (2010)Google Scholar
  15. 15.
    Cao, N., Yang, Z., Wang, C., Ren, K., Lou, W.: Privacy-preserving query over encrypted graph-structured data in cloud computing. vol. 6567, no. 6, pp. 393–402 (2011)Google Scholar
  16. 16.
    Wang, L., Shao, B., Xiao, Y., Wang, H.: How to partition a billion-node graph (2014)Google Scholar
  17. 17.
    Shao, B., Wang, H., Li, Y.: Trinity: a distributed graph engine on a memory cloud. In: ACM SIGMOD International Conference on Management of Data, pp. 505–516. ACM (2013)Google Scholar
  18. 18.
    Fan, W., Wang, X., Wu, Y.: Incremental graph pattern matching. ACM Trans. Database Syst. 38(3), 1–47 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Xiao-Lin Zhang
    • 1
    Email author
  • Hao-chen Yuan
    • 1
  • Zhuo-lin Li
    • 1
  • Huan-xiang Zhang
    • 1
  • Jian Li
    • 1
  1. 1.Inner Mongolia University of Science and TechnologyBaotouP. R. China

Personalised recommendations