Achieving k-Anonymity by Clustering in Attribute Hierarchical Structures
Jiuyong Li, Raymond Chi-Wing Wong, Ada Wai-Chee Fu, Jian Pei · Lecture notes in computer science · 2006
Individual privacy will be at risk if a published data set is not properly de-identified. k -anonymity is a major technique to de-identify a data set. A more general view of k -anonymity is clustering with a constraint of the minimum number of objects in every cluster. Most existing approaches to achieving k -anonymity by clustering are for numerical (or ordinal) attributes. In this paper, we study achieving k -anonymity by clustering in attribute hierarchical structures. We define generalisation distances between tuples to characterise distortions by generalisations and discuss the properties of the distances. We conclude that the generalisation distance is a metric distance. We propose an efficient clustering-based algorithm for k -anonymisation. We experimentally show that the proposed method is more scalable and causes significantly less distortions than an optimal global recoding k -anonymity method. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.