Controlled Rounding Problem
In Fischetti and Salazar (1998)
we describe theoretical models and practical solution
techniques for protecting confidentiality in statistical tables containing
sensitive information that cannot be disseminated. This is an issue
of primary importance in practice. We study the problem of protecting
sensitive information in a statistical table whose entries are subject
to any system of linear constraints.
This very general setting covers, among others, k-dimensional tables
with marginals as well as hierarchical and linked tables.
In particular, we address the NP-hard optimization problem known
in the literature as the (zero-restricted) Controlled Rounding Problem.
We also propose a modification of this problem, which allows for
enlarged rounding windows in case the zero-restricted version is proved
to have no feasible solution.
We describe integer Linear Programming (LP) models and
introduce effective LP-based enumerative algorithms.
Computational results on 2-, 3- and 4-dimensional tables are presented.
An interesting outcome is that 4-dimensional tables often admit
no zero-restricted rounding, whereas slightly enlarged rounding windows
produced feasible instances in all the cases in our test bed.
My contributions with M. Fischetti are:
Experiments with Controlled Rounding
for Statistical Disclosure Control
in Tabular Data with Linear Constraints
Journal of Official Statistics 14/4 (1998) 553-565.