Privacy-Preserving Scheme Against Inference Attack in Social Networks

  • Nidhi DesaiEmail author
  • Manik Lal Das
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 121)


Social networks analytics provide enormous business values for organizational and societal growth. Massive volumes of social network data get collected in every moment and released to other parties for various business objectives. The collected data play an important role in designing policies, plans and future projection of business strategies. These social network data carry sensitive information, and therefore, the adversary can exploit users profile and social relationships to disclose their privacy. Inference attack using the mining technique poses a crucial concern for privacy leakage in social networks. In this paper, we present a privacy-preserving scheme against inference attack. The proposed scheme adds spurious data in the published dataset such that sensitive information is not predicted using mining techniques. The proposed scheme is analyzed against a strong adversarial model, where an adversary is allowed to gather background knowledge from different sources. We have experimented the proposed scheme on real-social network dataset and the experimental results show that the privacy-preserving property of the proposed scheme outperforms in comparison with other related schemes.


Social networks Data privacy Inference attack Background knowledge 


  1. 1.
    Cai, Z., He, Z., Guan, X., Li, Y.: Collective data-sanitization for preventing sensitive information inference attacks in social networks. IEEE Trans. Dependable Secure Comput. 15(4), 577–590 (2018)Google Scholar
  2. 2.
    Qian, J., Li, X., Zhang, C., Chen, L., Jung, T., Han, J.: Social Network de-anonymization and privacy inference with knowledge graph model. IEEE Trans. Dependable Secure Comput. (2017)Google Scholar
  3. 3.
    Li, H., Chen, Q., Zhu, H., Ma, D., Wen, H., Shen, X.S.: Privacy leakage via de-anonymization and aggregation in heterogeneous social networks. IEEE Trans. Dependable Secure Comput. (2017)Google Scholar
  4. 4.
    Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 173–187 (2009)Google Scholar
  5. 5.
    Zhou, B., Pei, J., Luk, W.: A brief survey on anonymization techniques for privacy preserving publishing of social network data. ACM SIGKDD Explor. Newsl 10(2), 12–22 (2008)CrossRefGoogle Scholar
  6. 6.
    He, J., Chu, W.W., Liu, Z.: Inferring privacy information from social networks. In: Proceedings of Intelligence and Security Informatics, pp. 154–165 (2006)Google Scholar
  7. 7.
    Mislove, A., Viswanath, B., Gummadi, K., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 251–260 (2010)Google Scholar
  8. 8.
    Gong N., Liu, B.: Attribute inference attacks in online social networks. ACM Trans. Privacy Secur. 21(1) (2018)Google Scholar
  9. 9.
    Ryu, E., Rong, Y., Li, J., Machanavajjhala, A.: CURSO: protect yourself from curse of attribute inference. In: Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks, pp. 13–18Google Scholar
  10. 10.
    Zhong, Y., Jing Yuan, N., Zhong, W., Zhang, F., Xie, X.: You are where you go: inferring demographic attributes from location check-ins. In: WSDM (2015)Google Scholar
  11. 11.
    Nie, L. Zhang, L., Wang, M., Hong, R., Farseev, A., Chua, T.: Learning user attributes via mobile social multimedia analytics. 8(3) (2017)Google Scholar
  12. 12.
    Jurgens, D.: That’s what friends are for: Inferring location in online social media platforms based on social relationships. In: ICWSM, pp. 273–282 (2013)Google Scholar
  13. 13.
  14. 14.
    Farahbakhsh, R., Han, X., Cuevas, A., Crespi, N.: Analysis of publicly disclosed information in Facebook profiles. In: Proceedings of Advances in Social Networks Analysis and Mining, pp. 699–705 (2013)Google Scholar
  15. 15.
    Pawlak, Z.: Rough set theory and its applications to data analysis. J. Cybern. Syst. 29(7), 661–688 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.DA-IICTGandhinagarIndia

Personalised recommendations