Minimum distance controlled tabular adjustment (CTA) is a recent perturbative technique of statistical disclosure control for tabular data. Given a table to be protected, CTA looks for the closest safe table, using some particular distance. We focus on the continuous formulation of CTA, without binary variables, which results in a convex optimization problem for distances L 1, L 2 and L ∈∞∈. We also introduce the L 0-CTA problem, which results in a combinatorial optimization problem. The two more practical approaches, L 1-CTA (linear optimization problem) and L 2-CTA (quadratic optimization problem) are empirically compared on a set of public domain instances. The results show that, depending on the criteria considered, each of them is a better option. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Castro, J. (2012). Comparing L 1 and L 2 distances for CTA. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7556 LNCS, pp. 35–46). Springer Verlag. https://doi.org/10.1007/978-3-642-33627-0_4
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