Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
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)
Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 173–187 (2009)
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)
He, J., Chu, W.W., Liu, Z.: Inferring privacy information from social networks. In: Proceedings of Intelligence and Security Informatics, pp. 154–165 (2006)
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)
Gong N., Liu, B.: Attribute inference attacks in online social networks. ACM Trans. Privacy Secur. 21(1) (2018)
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. 13–18
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)
Nie, L. Zhang, L., Wang, M., Hong, R., Farseev, A., Chua, T.: Learning user attributes via mobile social multimedia analytics. 8(3) (2017)
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)
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)
Pawlak, Z.: Rough set theory and its applications to data analysis. J. Cybern. Syst. 29(7), 661–688 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Desai, N., Das, M.L. (2020). Privacy-Preserving Scheme Against Inference Attack in Social Networks. In: Singh, P., Pawłowski, W., Tanwar, S., Kumar, N., Rodrigues, J., Obaidat, M. (eds) Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019). Lecture Notes in Networks and Systems, vol 121. Springer, Singapore. https://doi.org/10.1007/978-981-15-3369-3_60
Download citation
DOI: https://doi.org/10.1007/978-981-15-3369-3_60
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3368-6
Online ISBN: 978-981-15-3369-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)