Abstract
Many applications of social networks require identity and/or relationship anonymity due to the sensitive, stigmatizing, or confidential nature of user identities and their behaviors. 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 chapter, we investigate how well an edge based graph randomization approach can protect node identities and sensitive links. Specifically, we quantify both identity disclosure and link disclosure when adversaries have one specific type of background knowledge (i.e., knowing the degrees of target individuals). Our theoretical studies and empirical evaluations show that edge randomization is a necessity in addition to node anonymization in order to preserve privacy in the released graph.
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Ying, X., Wu, X., Pan, K., Guo, L. (2009). On the Quantification of Identity and Link Disclosures in Randomizing Social Networks. In: Ras, Z.W., Ribarsky, W. (eds) Advances in Information and Intelligent Systems. Studies in Computational Intelligence, vol 251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04141-9_5
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DOI: https://doi.org/10.1007/978-3-642-04141-9_5
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