Experiments with Controlled Rounding for Statistical Disclosure Control in Tabular Data with Linear Constraints

Matteo Fischetti and Juan-José Salazar-González

Abstract:
In this article 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 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, which have been embedded within ARGUS, a software package for statistical disclosure control. 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.

Keywords:
Statistical disclosure control; confidentiality; controlled rounding; integer linear programming.