Journal of Official Statistics, Vol.14, No.4, 1998. pp. 421–435
Optimal Local Suppression in Microdata
A.G. de Waal and L.C.R.J. Willenborg
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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