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ICDM
2006
IEEE

Optimal k-Anonymity with Flexible Generalization Schemes through Bottom-up Searching

13 years 9 months ago
Optimal k-Anonymity with Flexible Generalization Schemes through Bottom-up Searching
In recent years, a major thread of research on kanonymity has focused on developing more flexible generalization schemes that produce higher-quality datasets. In this paper we introduce three new generalization schemes that improve on existing schemes, as well as algorithms enumerating valid generalizations in these schemes. We also introduce a taxonomy for generalization schemes and a new cost metric for measuring information loss. We present a bottom-up search strategy for finding optimal anonymizations. This strategy works particularly well when the value of k is small. We show the feasibility of our approach through experiments on real census data.
Tiancheng Li, Ninghui Li
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where ICDM
Authors Tiancheng Li, Ninghui Li
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