Primal-dual interior-point methods
Primal-dual interior-point methods
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
A tool for analyzing and fixing infeasible RCTA instances
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
Fast Solution of -Norm Minimization Problems When the Solution May Be Sparse
IEEE Transactions on Information Theory
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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 L1, L2 and L∞. We also introduce the L0-CTA problem, which results in a combinatorial optimization problem. The two more practical approaches, L1-CTA (linear optimization problem) and L2-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.