K-Community Anonymity Approach for Social Network Data

doi:10.23940/ijpe.20.10.p18.16651673

Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (10): 1665-1673.doi: 10.23940/ijpe.20.10.p18.16651673

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K-Community Anonymity Approach for Social Network Data

Guoqiang Gong, Xin Cao, Ye Jin, Xiaobo Ding, and Ke Lv^*

School of Computer and Information, Three Gorges University, Yichang, 443002, China

Submitted on ; Revised on ; Accepted on
Contact: * E-mail address: gonggq_shh@163.com
About author:Guoqiang Gong is an associate professor of the school of computer & information of China Three Gorges University. He received M.S. degrees in control theory and control engineering from China Three Gorges University in 2005 and the Ph.D. degree from China Tongji University in 2010. His research interests include privacy preservation, advanced signal processing.
Xin Cao is a master degree candidate of the School of computer & information of China Three Gorges University. His research interests include deep learning and social network data analysis.
Ye Jin is a master of the School of computer & information of China Three Gorges University. His research interests include social network and privacy protection.
Xiaobo Ding is an associate professor of the school of computer & information of China Three Gorges University. His research interests include computer application and big data processing.
Ke Lv is a professor of the school of computer & information of China Three Gorges University. His research interests include information processing and big data analysis.

Abstract

Abstract: Different from the traditional privacy protection of relational data, in this paper we focus on the protection of graph data in social networks. The problem of identity disclosure on graph data publication in social networks has caused an increase in attention and many existing methods of protecting graph data are based on the properties of the vertices. These methods can resist invasions such as degree attacks or neighbor attacks. Once an adversary knows the structural information of the graph, the probability of a vertex being recognized will be greatly increased. Considering the community structure of the graph, we propose a k-community anonymity model, in which the probability of an adversary identifying a vertex is no more than 1/k. We conduct our experiments in real social network datasets and compare it with the traditional k-degree anonymity model. The results show that the new protection scheme has better anonymous performance on resisting the structural attacks and a greater impact on the community structure in the graph.

Key words: privacy protection, social networks, graph structure, community anonymity

Guoqiang Gong, Xin Cao, Ye Jin, Xiaobo Ding, and Ke Lv. K-Community Anonymity Approach for Social Network Data [J]. Int J Performability Eng, 2020, 16(10): 1665-1673.

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References

1. R. Heatherly, M. Kantarcioglu,B. Thuraisingham, “Preventing Private Information Inference Attacks on Social Networks,” inProceedings of IEEE Transactions on Knowledge and Data Engineering, pp. 1849-1862, August 2013
2. W. M. Campbell, C. K. Dagli,C. J. Weinstein, “Social Network Analysis with Content and Graphs,” inProceedings of Lincoln Laboratory Journal, pp. 61-81, January 2013
3. X. Wu, X. Ying, K. Liu K, et al., “A Survey of Privacy-Preservation of Graphs and Social Networks,” inProceedings of Managing and Mining Graph Data, pp. 421-453, Springer, US, 2010
4. S. Ji, P. Mittal,R. Beyah, “Graph Data Anonymization, De-Anonymization Attacks, and De-Anonymizability Quantification: A Survey,” IEEE Communications Surveys and Tutorials, Vol. 19, No. 2, pp. 1305-1326, 2017
5. C. H. Tai, P. S. Yu, D. N. Yang,M. S. Chen, “Privacy-Preserving Social Network Publication Against Friendship Attacks,” inProceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1262-1270, August 2011
6. L. Sweeney, “K-Anonymity: A model for Protecting Privacy,” Journal of Uncertainty, Fuzziness and Knowledge-based Systems, Vol. 10, No. 5, pp. 557-570, 2002
7. J. Casas-Roma, J. Herrera-Joancomartí,V. Torra, “k-Degree Anonymity and Edge Selection: Improving Data Utility in Large Networks,” Knowledge and Information Systems, Vol. 50, No. 2, pp. 447-474, 2017
8. L. Zou, L. Chen,M. T. Özsu, “K-Automorphism: A General Framework for Privacy Preserving Network Publication,” Proceedings of the VLDB Endowment, Vol. 2, No. 1, pp. 946-957, 2009
9. J. H. Abawajy, M. I. H.Ninggal, and T. Herawan, “Privacy Preserving Social Network Data Publication,” IEEE Communications Surveys and Tutorials, Vol. 18, No. 3, pp. 1974-1997, 2016
10. M. Girvan and M. E. J. Newman, “Community Structure in Social and Biological Networks,” Proceedings of the National Academy of Sciences, Vol. 99, No. 12, pp. 7821-7826, 2002
11. M. E. J.Newman, “Modularity and Community Structure in Networks,” Proceedings of the National Academy of Sciences, Vol. 103, No. 23, pp. 8577-8582, 2006
12. K. Liu and E. Terzi, “Towards Identity Anonymization on Graphs,” in Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 93-106, 2008
13. X. K. Zhang, S. Fei, C. Song, et al., “Label Propagation Algorithm based on Local Cycles for Community Detection,” International Journal of Modern Physics B, Vol. 29, No. 5, pp. 15-29, 2015
14. L. Waltman and N. J. van Eck, “A Smart Local Moving Algorithm for Large-Scale Modularity-based Community Detection,” The European Physical Journal B, Vol. 86, No. 11, pp. 471, 2013
15. A. Praveen and S. Smys, “Anonymization in Social Networks: A Survey on the Issues of Data Privacy in Social Network Sites,” International Journal of Engineering and Computer Science, Vol. 5, No. 3, pp. 15912-15918, 2016
16. F. Yu, M. Chen, B. Yu, et al., “Privacy Preservation based on Clustering Perturbation Algorithm for Social Network,” Multimedia Tools and Applications, Vol. 77, No. 9, pp. 11241-11258, 2018
17. Y. Lv, T. Ma, M. Tang, et al., “An Efficient and Scalable Density-based Clustering Algorithm for Datasets with Complex Structures,”Neurocomputing, Vol. 171, pp. 9-22, 2016
18. J. Casas-Roma, J. Herrera-Joancomartí,V. Torra, “k-Degree Anonymity and Edge Selection: Improving Data Utility in Large Networks,” Knowledge and Information Systems, Vol. 50, No. 2, pp. 447-474, 2017
19. T. Tassa and D. J. Cohen, “Anonymization of Centralized and Distributed Social Networks by Sequential Clustering,” IEEE Transactions on Knowledge and Data Engineering, Vol. 25, No. 2, pp. 311-324, 2013
20. M. N. Huda, S. Yamada S,N. Sonehara, “An Efficient k-Anonymization Algorithm with Low Information Loss,”Lecture Notes in Electrical Engineering, Vol. 156, pp. 249-254, 2013
21. J. Eustace and X. Wang, “Overlapping Community Detection using Neighborhood Ratio Matrix,”Physica A: Statistical Mechanics and its Applications, Vol. 421, pp. 510-521, 2015
22. T. Ma, Y. Wang, M. Tang, et al., “LED: A Fast Overlapping Communities Detection Algorithm based on Structural Clustering,” Neurocomputing, Vol. 20, No. 7, pp. 488-500, 2016
23. H. Rong, T. Ma, M. Tang,J. Cao, “A Novel Subgraph K+-Isomorphism Method in Social Network based on Graph Similarity Detection,” Soft Computing, Vol. 22, No. 8, pp. 2583-2601, 2017
24. L. Tabourier, A. S. Libert,R. Lambiotte, “Predicting Links in Ego-Networks using Temporal Information,” EPJ Data Science, Vol. 5, No. 1, pp. 1, 2016
25. X. L.Zhang and Y. Tang, “Protecting Encrypted Data Against Inference Attacks in Outsourced Databases,” Applied Mechanics and Materials, Vol. 57, No. 1, pp. 621-625, 2014

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K-Community Anonymity Approach for Social Network Data

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