Preserving the privacy of sensitive relationships in graph data

TitlePreserving the privacy of sensitive relationships in graph data
Publication TypeConference Papers
Year of Publication2008
AuthorsZheleva E, Getoor L
Conference NameProceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Date Published2008///
Conference LocationBerlin, Heidelberg
ISBN Number3-540-78477-2, 978-3-540-78477-7
Keywordsanonymization, graph data, identification, link mining, noisy-or, privacy, social network analysis

In this paper, we focus on the problem of preserving the privacy of sensitive relationships in graph data. We refer to the problem of inferring sensitive relationships from anonymized graph data as link reidentification. We propose five different privacy preservation strategies, which vary in terms of the amount of data removed (and hence their utility) and the amount of privacy preserved. We assume the adversary has an accurate predictive model for links, and we show experimentally the success of different link re-identification strategies under varying structural characteristics of the data.