Abstract
Many applications of social networks require relationship anonymity due to the sensitive, stigmatizing, or confidential nature of relationship. Recent work showed that the simple technique of anonymizing graphs by replacing the identifying information of the nodes with random ids does not guarantee privacy since the identification of the nodes can be seriously jeopardized by applying subgraph queries. In this paper, we investigate how well an edge based graph randomization approach can protect sensitive links. We show via theoretical studies and empirical evaluations that various similarity measures can be exploited by attackers to significantly improve their confidence and accuracy of predicted sensitive links between nodes with high similarity values.
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References
Backstrom, L., Dwork, C., Kleinberg, J.: Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In: WWW 2007: Proceedings of the 16th international conference on World Wide Web, pp. 181–190. ACM Press, New York (2007)
Campan, A., Truta, T. M.: A clustering approach for data and structural anonymity in social networks. In: PinKDD (2008)
Hay, M., Miklau, G., Jensen, D., Towsely, D., Weis, P.: Resisting structural re-identification in anonymized social networks. In: VLDB (2008)
Hay, M., Miklau, G., Jensen, D., Weis, P., Srivastava, S.: Anonymizing social networks. University of Massachusetts Technical Report, 07-19 (2007)
Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: CIKM 2003: Proceedings of the twelfth international conference on Information and knowledge management, pp. 556–559. ACM, New York (2003)
Liu, K., Terzi, E.: Towards identity anonymization on graphs. In: Proceedings of the ACM SIGMOD Conference, Vancouver, Canada. ACM Press, New York (2008)
Shetty, J., Adibi, J.: The Enron email dataset database schema and brief statistical report. Information Sciences Institute Technical Report, University of Southern California (2004)
Ying, X., Wu, X.: Randomizing social networks: a spectrum preserving approach. In: Proc. of the 8th SIAM Conference on Data Mining (April 2008)
Ying, X., Wu, X.: Graph generation with prescribed feature constraints. In: Proc. of the 9th SIAM Conference on Data Mining (2009)
Zheleva, E., Getoor, L.: Preserving the privacy of sensitive relationships in graph data. In: Bonchi, F., Ferrari, E., Malin, B., Saygın, Y. (eds.) PInKDD 2007. LNCS, vol. 4890, pp. 153–171. Springer, Heidelberg (2008)
Zhou, B., Pei, J.: Preserving Privacy in Social Networks Against Neighborhood Attacks. Data Engineering, 2008. In: ICDE 2008. IEEE 24th International Conference on, pp. 506–515 (2008)
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Ying, X., Wu, X. (2009). On Link Privacy in Randomizing Social Networks. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_6
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DOI: https://doi.org/10.1007/978-3-642-01307-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01306-5
Online ISBN: 978-3-642-01307-2
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