Title

Hybrid Microdata via Model-Based Clustering

Document Type

Contribution to Book

Publication Date

2012

Publication Title

Privacy in Statistical Databases, Lecture Notes in Computer Science 7556

DOI

10.1007/978-3-642-33627-0_9

Abstract

In this paper we propose a new scheme for statistical disclosure limitation which can be classified as a hybrid method of protection, that is, a method that combines properties of perturbative and synthetic methods. This approach is based on model-based clustering with the subsequent synthesis of the records within each cluster. The novelty is that the clustering and synthesis methods have been carefully chosen to fit each other in view of reducing information loss. The model-based clustering tries to obtain clusters such that the within-cluster data distribution is approximately normal; then we can use a multivariate normal synthesizer for the local synthesis of data. In this way, some of the non-normal characteristics of the data are captured by the clustering, so that a simple synthesizer for normal data can be used within each cluster. Our method is shown to be effective when compared to other disclosure limitation strategies.

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