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Resisting re-identification mining on social graph data

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Abstract

Varieties of sensitive personal information become a privacy concern for social networks. However, characteristics of social graphs could be utilized by attackers to re-identify target entities of social networks. In this paper, we first analyze a new attack model named bin-based attack, which re-identifies social individuals in social networks, according to their graph structure characteristics. For bin-based attack, we propose a novel k-anonymity scheme. With this scheme, social individuals are completely k-anonymity protection. Experiments illustrate the effectiveness of the proposed scheme. The utility of anonymized networks are demonstrated with the results of vertex degree, and betweenness.

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References

  1. Chiasserini, C.F., Garetto, M., Leonardi, E.: Social network de-anonymization under scale-free user relations. IEEE/ACM Trans. Networking 24(6), 3756–3769 (2016)

    Article  Google Scholar 

  2. Gao, J., Liu, P., Kang, X., Zhang, L., Wang, J.: PRS: Parallel relaxation simulation for massive graphs. Comput. J. 59(6), 848–860 (2015)

    Article  MathSciNet  Google Scholar 

  3. Gao, J., Song, B., Ke, W., Hu, X.: Balanceali: multiple PPI network alignment with balanced high coverage and consistency. IEEE Trans. Nanobioscience 16(5), 333–340 (2017)

    Article  Google Scholar 

  4. Gao, J., Wang, J., Zhong, P., Wang, H.: On threshold free error detection for industrial wireless sensor networks. IEEE Trans. Ind. Inf. (2017). https://doi.org/10.1109/TII.2017.2785395

    Article  Google Scholar 

  5. http://snap.stanford.edu/data/index.html

  6. Hu, R., Zhu, X., Cheng, D., He, W., Yan, Y., Song, J., Zhang, S.: Graph self-representation method for unsupervised feature selection. Neurocomputing 220, 130–137 (2017)

    Article  Google Scholar 

  7. Ji, S., Li, W., Srivatsa, M., Beyah, R.: Structural data de-anonymization: theory and practice. IEEE/ACM Trans. Networking 24(6), 3523–3536 (2016)

    Article  Google Scholar 

  8. Koutra, D., Tong, H., Lubensky, D.: Big-align: fast bipartite graph alignment. In: Proceeding of IEEE 13th international conference on data mining (ICDM), pp 389–398 (2013)

  9. Liu, Q., Wang, G., Li, F., Yang, S., Wu, J.: Preserving privacy with probabilistic indistinguishability in weighted social networks. IIEEE Trans. Parallel Distrib. Syst. 28(5), 1417–1429 (2017)

    Article  Google Scholar 

  10. Maag, M.L., Denoyer, L., Gallinari, P.: Graph anonymization using machine learning. In: IEEE 28th international conference on proceeding of advanced information networking and applications (AINA), pp 1111–1118 (2014)

  11. Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: l-Diversity: privacy beyond k-anonymity. In: Proceedings of international conference on data engineering, pp 1–12 (2006)

  12. Peng, W., Li, F., Zou, X., Wu, J.: A two-stage deanonymization attack against anonymized social networks. IEEE Trans. Comput. 63(2), 290–303 (2014)

    Article  MathSciNet  Google Scholar 

  13. Su, Z., Xu, Q., Qi, Q.: Big data in mobile social networks: a QoE-oriented framework. IEEE Netw. 30(1), 52–57 (2016)

    Article  Google Scholar 

  14. Sun, C., Philip, S.Y., Kong, X., Fu, Y.: Privacy preserving social network publication against mutual friend attacks. In: Proceeding of IEEE 13th international conference on data mining, pp 883–890 (2013)

  15. Tassa, T., Cohen, D. J.: Anonymization of centralized and distributed social networks by sequential clustering. IEEE Trans. Knowl. Data Eng. 25(2), 311–324 (2013)

    Article  Google Scholar 

  16. Tang, J., Lou, T., Kleinberg, J.: Inferring social ties across heterogenous networks. In: Proceedings of ACM international conference on Web search and data mining, pp 743–752 (2012)

  17. Wang, G., Liu, Q., Li, F., Yang, S., Wu, J.: Outsourcing privacy-preserving social networks to a cloud. In: Proceedings of Infocom, pp 2886–2894 (2013)

  18. Yuan, M., Chen, L., Philip, S.Y., Yu, T.: Protecting sensitive labels in social network data anonymization. IEEE Trans. Knowl. Data Eng. 25(3), 633–647 (2013)

    Article  Google Scholar 

  19. Zhang, J. , Philip, S.Y.: Multiple anonymized social networks alignment. In: Proceeding of IEEE 13th international conference on data mining (ICDM), pp 599–608 (2015)

  20. Zhang, X., Yang, L. T., Liu, C., Chen, J.: A scalable two-phase top-down specialization approach for data anonymization using mapreduce on cloud. IEEE Trans. Parallel Distrib. Syst. 25(2), 363–373 (2014)

    Article  Google Scholar 

  21. Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: Proceedings of international conference on data engineering, pp 506–515 (2008)

  22. Zhu, X., Li, X., Zhang, S., Ju, C., Wu, X.: Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Transactions on Neural Networks and Learning Systems 28(6), 1263–1275 (2017)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. College of Information Science and Engineering, Central South University, Changsha, 410083, China

    Jianliang Gao & Jianxin Wang

  2. College of Computing and Informatics, Drexel University, Philladephia, 19104, USA

    Qing Ping

Authors
  1. Jianliang Gao
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  2. Qing Ping
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  3. Jianxin Wang
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Corresponding author

Correspondence to Jianxin Wang.

Additional information

This article belongs to the Topical Collection: Special Issue on Deep Mining Big Social Data

Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell

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Gao, J., Ping, Q. & Wang, J. Resisting re-identification mining on social graph data. World Wide Web 21, 1759–1771 (2018). https://doi.org/10.1007/s11280-017-0524-3

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  • DOI: https://doi.org/10.1007/s11280-017-0524-3

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