TY - CONF T1 - Preserving the privacy of sensitive relationships in graph data T2 - Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD Y1 - 2008 A1 - Zheleva,Elena A1 - Getoor, Lise KW - anonymization KW - graph data KW - identification KW - link mining KW - noisy-or KW - privacy KW - social network analysis AB - 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. JA - Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD T3 - PinKDD'07 PB - Springer-Verlag CY - Berlin, Heidelberg SN - 3-540-78477-2, 978-3-540-78477-7 UR - http://dl.acm.org/citation.cfm?id=1793474.1793485 ER -