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2007
ACM

Preserving the Privacy of Sensitive Relationships in Graph Data

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Preserving the Privacy of Sensitive Relationships in Graph Data
Abstract. 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.
Elena Zheleva, Lise Getoor
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2007
Where KDD
Authors Elena Zheleva, Lise Getoor
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