Preserving the privacy of sensitive relationships in graph data

被引：0

作者：

Zheleva, Elena ^{[1
]}

Getoor, Lise ^{[1
]}

机构：

[1] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA

来源：

PRIVACY, SECURITY, AND TRUST IN KDD | 2008年 / 4890卷

关键词：

privacy; anonymization; identification; link mining; social network analysis; noisy-or; graph data;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：153 / 171

页数：19

共 18 条

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