TitleGeneralization-Based k-Anonymization
Publication TypeConference Proceedings
Year of Conference2015
AuthorsArmengol E, Torra V
EditorTorra V., Narukawa Y.
Volume5861
PublisherSpriger-Verlag
Pagination207-218
Conference LocationSkövde, Sweden
Date Published21/09/2015
Keywordsgeneralization, K-Anonymity
Abstract

Microaggregation is an anonymization technique consisting on partitioning the data into clusters no smaller than k elements and then replacing the whole cluster by its prototypical representant. Most of microaggregation techniques work on numerical attributes. However, many data sets are described by heterogeneous types of data, i.e., nu- merical and categorical attributes. In this paper we propose a new mi- croaggregation method for achieving a compliant k-anonymous masked file for categorical microdata based on generalization. The goal is to build a generalized description satisfied by at least k domain objects and to replace these domain objects by the description. The way to construct that generalization is similar that the one used in growing decision trees. Records that cannot be generalized satisfactorily are discarded, therefore some information is lost. In the experiments we performed we prove that the new approach gives good results.