Abstract
The published multi-social network graphs contain numerous private information. To protect these information, researchers try to simulate attack models and design protection schemes. In this paper, we propose a heuristic attack model based on Dopv (Degree of paired vertices) attack. The attacker by defrauding trust or browse homepage to acquires the victim’s degrees (number of friends) from two published social network graphs and combine them into Dopv. Based on Dopv attack, attacker locates target candidates then compare nodes similarity by the same attributes or labels to find out target. To avoid this attack and protect the individuals’ privacy, we propose a new solution called Pvk-degree anonymity (Paired vertices k-degree anonymous). In Pvk-degree anonymity, the probability of a real user being re-identified is no more than 1/k. We devise algorithms to achieve the Pvk-degree anonymity that preserves the original vertex set in the sense that we allow the edge modified but no deletion of vertices. The experimental results show that our approach can preserve the privacy and guarantee the utility of social network graphs effectively against Dopv attacks.
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
References
Wu, X., Ying, X., Liu, K., et al.: A survey of algorithms for privacy-preservation of graphs and social networks. In: Managing & Mining Graph Data Chapter, pp. 421–453 (2009)
Casas-Roma, J., Herrera-Joancomartí, J., Torra, V.: A survey of graph-modification techniques for privacy-preserving on networks. Artif. Intell. Rev. 47(3), 1–26 (2016)
Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: ACM SIGMOD International Conference on Management of Data, pp. 93–106. ACM (2008)
Narayanan, A., Shmatikov, V:. De-anonymizing Social Networks. In: IEEE Symposium on Security and Privacy, 2009, pp. 173–187. IEEE (2009)
Casas-Roma, J.: An algorithm for k-degree anonymity on large networks. In: International Conference on Advances in Social Networks Analysis and Mining, pp. 671–675. IEEE (2013)
Casas-Roma, J., Herrera-Joancomartí, J.: k-Degree anonymity and edge selection: improving data utility in large networks. Knowl. Inf. Syst. 50(2), 1–28 (2016)
Bredereck, R., Froese, V., Hartung, S., et al.: The complexity of degree anonymization by vertex addition. Theoret. Comput. Sci. 607(P1), 16–34 (2015)
Tai, C.H., Yu, P.S., Yang, D.N., et al.: Privacy-preserving social network publication against friendship attacks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1262–1270. ACM (2011)
Sun, C., et al.: Privacy preserving social network publication against mutual friend attacks. In IEEE, International Conference on Data Mining Workshops, pp. 71–97. IEEE (2014)
Assam, R., Hassani, M., Brysch, M., et al.: (k, d)-Core anonymity: Structural anonymization of massive networks. ACM (2014)
Ma, T., Zhang, Y., Cao, J., et al.: KDVEM: a k-degree anonymity with vertex and edge modification algorithm. Computing 97(12), 1165–1184 (2015)
Wu, H.: A Clustering Bipartite Graph Anonymous Method for Social Networks. J. Inf. Comput. Sci. 10(18), 6031–6040 (2013)
Liu, C., Yin, D., Li, H., Wang, W., Yang, W.: Preserving privacy in social networks against label pair attacks. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds.) WASA 2017. LNCS, vol. 10251, pp. 381–392. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60033-8_34
Chakrabarti, D., et al.: R-MAT: a recursive model for graph mining. In: Siam International Conference on Data Mining, Lake Buena Vista, Florida, USA. DBLP, April 2004
Li, Y., Shen, H.: Anonymizing graphs against weight-based attacks. In: IEEE International Conference on Data Mining Workshops, pp. 491–498. IEEE Computer Society (2010)
Bhagat, S., Cormode, G., Krishnamurthy, B., et al.: Class-based graph anonymization for social network data[J]. Proc. Vldb Endow. 2(1), 766–777 (2009)
Liu, X., Yang, X.: A generalization based approach for anonymizing weighted social network graphs. In: International Conference on Web-Age Information Management, pp. 118–130. Springer-Verlag (2011)
Zhu, T., Wang, S., Li, X., et al.: Structural attack to anonymous graph of social networks. Math. Probl. Eng. 2013(2), 1–8, 21 November 2013
Tai, C.H., Yu, P.S., Yang, D.N., et al.: Structural diversity for privacy in publishing social networks. In: Eleventh Siam International Conference on Data Mining, SDM 2011, April 28–30, 2011, Mesa, Arizona, USA, pp. 35–46. DBLP (2012)
Hakimi, S.L.: On realizability of a set of integers as degrees of the vertices of a linear graph. I. J. Soc. Ind. 10(3), 496–506 (1962)
Wasserman, S., Faust, K.: Social Network Analysis. Encycl. Soc. Netw. Anal. Min. 22(Suppl 1), 109–127 (1994)
Acknowledgements
This work is supported by National Natural Science Foundation of China under Grant 61572459, 61672180. The paper is funded by the International Exchange Program of Harbin Engineering University for Innovation-oriented Talents Cultivation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Fu, Y., Wang, W., Fu, H., Yang, W., Yin, D. (2018). Privacy Preserving Social Network Against Dopv Attacks. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11233. Springer, Cham. https://doi.org/10.1007/978-3-030-02922-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-02922-7_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02921-0
Online ISBN: 978-3-030-02922-7
eBook Packages: Computer ScienceComputer Science (R0)