Advertisement

A Hierarchical k-Anonymous Technique of Graphlet Structural Perception in Social Network Publishing

  • Dongran Yu
  • Huaxing Zhao
  • Li-e Wang
  • Peng Liu
  • Xianxian Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11005)

Abstract

The structural information of social network data plays an important role in many fields of research. Therefore, privacy-preserving social network publication methods should preserve more structural information, such as the higher-order organizational structure of complex networks (graphlets/motifs). Therefore, how to preserve the graphlet structure information in a social network as much as possible becomes a key problem in social network privacy protection. In this paper, to address the problem of excessive loss of graphlet structural information in the privacy process of published social network data, we proposed a technique of hierarchical k-anonymity for graphlet structural perception. The method considers the degree of social network nodes according to the characteristics of the power-law distribution. The nodes are divided according to the degrees, and the method analyzes the graphlet structural features of the graph in the privacy process and adjusts the privacy-processing strategies of the edges according to the graphlet structural features. This is done, in order to meet the privacy requirement while protecting the graphical structural information in the social network and, improving the utility of the data. This paper uses two real public data sets, WebKB and Cora, and conducted experiments and evaluations. Finally, the experimental results show that the method proposed in this paper can concurrently provide the same privacy protection intensity, better maintain the social network’s structural information and improve the data’s utility.

Keywords

Social networks Graphlet Privacy protection Hierarchical k-anonymity 

Notes

Acknowledgment

The research is supported by the National Science Foundation of China (Nos. 61672176, 61662008, 61502111), Guangxi “Bagui Scholar” Teams for Innovation and Research Project, the Guangxi Collaborative Center of Multi-source Information Integration and Intelligent Processing, Guangxi Natural Science Foundation (Nos. 2015GXNSFBA139246, 2016GXNSFAA380192), and the Innovation Project of Guangxi Graduate Education (Nos. YCSZ2015104, 2018KY0082).

References

  1. 1.
    Hay, M., Miklau, G., Jensen, D., Towsley, D.: Resisting structural re-identification in anonymized social networks. Proc. VLDB Endow. 1(1), 102–114 (2008)CrossRefGoogle Scholar
  2. 2.
    Campan, A., Truta, T.M.: Data and structural k-anonymity in social networks. In: Bonchi, F., Ferrari, E., Jiang, W., Malin, B. (eds.) PInKDD 2008. LNCS, vol. 5456, pp. 33–54. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-01718-6_4CrossRefGoogle Scholar
  3. 3.
    Tassa, T., Cohen, D.: Anonymization of centralized and distributed social networks by sequential clustering. IEEE Trans. Knowl. Data Eng. 25(2), 311–324 (2012)CrossRefGoogle Scholar
  4. 4.
    Cormode, G., Srivastava, D., Bhagat, S., Krishnamurthy, B.: Class-based graph anonymization for social network data. PVLDB 2(1), 766–777 (2009)Google Scholar
  5. 5.
    Cormode, G., Srivastava, D., Yu, T., Zhang, Q.: Anonymizing bipartite graph data using safe groupings. PVLDB 1(1), 833–844 (2008)Google Scholar
  6. 6.
    Zheleva, E., Getoor, L.: Preserving the privacy of sensitive relationships in graph data. In: Bonchi, F., Ferrari, E., Malin, B., Saygin, Y. (eds.) PInKDD 2007. LNCS, vol. 4890, pp. 153–171. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-78478-4_9CrossRefGoogle Scholar
  7. 7.
    Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: SIGMOD Conference, pp. 93–106 (2008)Google Scholar
  8. 8.
    Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: ICDE, pp. 506–515 (2008)Google Scholar
  9. 9.
    Thompson, B., Yao, D.: The union-split algorithm and cluster-based anonymization of social networks. In: ASIACCS, pp. 218–227 (2009)Google Scholar
  10. 10.
    Zou, L., Chen, L., Özsu, M.T.: K-automorphism: a general framework for privacy preserving network publication. PVLDB 2(1), 946–957 (2009)Google Scholar
  11. 11.
    Wu, W., Xiao, Y., Wang, W., He, Z., Wang, Z.: k-symmetry model for identity anonymization in social networks. In: EDBT, pp. 111–122 (2010)Google Scholar
  12. 12.
    Hay, M., Miklau, G., Jensen, D., Weis, P., Srivastava, S.: Anonymizing social networks. University of Massachusetts, Technical report 07-19 (2007)Google Scholar
  13. 13.
    Ying, X., Pan, K., Wu, X., Guo, L.: Comparisons of randomization and k-degree anonymization schemes for privacy preserving social network publishing. In: SNA-KDD, pp. 1–10 (2009)Google Scholar
  14. 14.
    Casas-Roma, J., Herrera-Joancomartí, J., Torra, V.: k-degree anonymity and edge selection: improving data utility in large networks. Knowl. Inf. Syst. 50, 447 (2017)CrossRefGoogle Scholar
  15. 15.
    Alon, N., Yuster, R., Zwick, U.: Finding and counting given length cycles. Algorithmica 17(3), 209–223 (1997)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationship of the internet topology. In: ACM SIGCOMM. ACM Press, Cambridge (1999)CrossRefGoogle Scholar
  17. 17.
    Wang, H.: Research on anonymous method of effectively preserving the community structure for social network data publication. Guangxi Normal University, Guilin (2016)Google Scholar
  18. 18.
    Task, C., Clifton, C.: A guide to differential privacy theory in social network analysis. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 411–417. IEEE (2013)Google Scholar
  19. 19.
    Benson, A.R., Gleich, D.F., Leskovec, J.: Higher-order organization of complex networks. Science 353(6295), 163–166 (2016)CrossRefGoogle Scholar
  20. 20.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar
  21. 21.
    Li, A., Pan, Y.: Structural information and dynamical complexity of networks. IEEE Trans. Inf. Theory 62(6), 3290–3339 (2016)MathSciNetCrossRefGoogle Scholar
  22. 22.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Guangxi Key Lab of Multi-source Information Mining and SecurityGuangxi Normal UniversityGuilinChina

Personalised recommendations