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Title: Extended k-anonymity models against sensitive attribute disclosure
Citation Type: Journal Article
Publication Year: 2011
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Abstract: p-Sensitive k-anonymity model has been recently defined as a sophistication of k-anonymity. This new property requires that there be at least p distinct values for each sensitive attribute within the records sharing a set of quasi-identifier attributes. In this paper, we identify the situations when the p-sensitive k-anonymity property is not enough for the sensitive attributes protection. To overcome the shortcoming of the p-sensitive k-anonymity principle, we propose two new enhanced privacy requirements, namelyp+-sensitive k-anonymity and p,a-sensitive k-anonymity properties. These two new introduced models target at different perspectives. Instead of focusing on the specific values of sensitive attributes, p+-sensitive k-anonymity model concerns more about the categories that the values belong to. Although (p,a)-sensitive k-anonymity model still put the point on the specific values, it includes an ordinal metric system to measure how much the specific sensitive attribute values contribute to each QI-group. We make a thorough theoretical analysis of hardness in computing the data set that satisfies either pa-sensitive k anonymity or (p,a)-sensitive k-anonymity. We devise a set of algorithms using the idea of top-down specification, which is clearly illustrated in the paper. We implement our algorithms on two real-world data sets and show in the comprehensive experimental evaluations that the two new introduced models are superior to the previous method in terms of effectiveness and efficiency.
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Authors: Wang, Hua; Sun, Lili; Sun, Xiaoxun
Periodical (Full): Computer Communications
Issue: 4
Volume: 34
Pages: 526-535
Data Collections: IPUMS USA
Topics: Other
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