JOS

Abstract
Journal of Official Statistics, Vol.14, No.4, 1998. pp. 421435

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Optimal Local Suppression in Microdata

Abstract:
In this article we assume that a safe microdata set has to be produced by a statistical office, for release to external researchers. To check the safety of such a microdata set, we assume that the statistical office checks the frequency of certain combinations of values. If a combination occurs frequently enough in the file, it is considered safe, otherwise unsafe. Unsafe combinations can be eliminated from the file by using techniques such as global recoding (=combining several categories of a variable into a single one) and local suppression (=replacing the value of a variable in a record by a missing value). In practice one first applies global recodings interactively to reduce the initial number of unsafe combinations drastically. Possible remaining unsafe combinations in the microdata set are then eliminated automatically through the application of local suppressions. The present article concentrates on this second step, i.e., the elimination of unsafe combinations by local suppressions, in an optimal way. In particular several optimal local suppression models are formulated and studied. The aim of these models is to apply local suppression in an optimal way, under various constraints. All these local suppression models turn out to be set-covering problems.

Keywords:
Statistical disclosure control; microdata; local suppression; integer programming; set-covering.

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