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.

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Tiancheng Li, Ninghui Li
• Keywords: Bottom-up Search Strategy, Data Mining, Generalization Schemes, ICDM 2006, flexible Generalization Schemes