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Computational experiments with minimum-distance controlled perturbation methods

Author
Castro, J.
Type of activity
Journal article
Journal
Lecture notes in computer science
Date of publication
2004-06
Volume
3050
First page
73
Last page
86
DOI
https://doi.org/10.1007/978-3-540-25955-8_6 Open in new window
URL
https://link.springer.com/chapter/10.1007/978-3-540-25955-8_6 Open in new window
Abstract
Minimum-distance controlled perturbation is a recent family of methods for the protection of statistical tabular data. These methods are both efficient and versatile, since can deal with large tables of any structure and dimension, and in practice only need the solution of a linear or quadratic optimization problem. The purpose of this paper is to give insight into the behaviour of such methods through some computational experiments. In particular, the paper (1) illustrates the theoretical resul...
Keywords
Controlled perturbation methods, Linear programming, Quadratic programming, Statistical disclosure control
Group of research
GNOM - Mathematical Optimization Group

Participants