Abstract
Minimum distance controlled tabular adjustment (CTA) is an emerging perturbative method of statistical disclosure control for tabular data. The goal of CTA is to find the closest safe table to some original tabular data with sensitive information. Closeness is usually measured by ℓ1 or ℓ2 distances. Distance ℓ1 provides solutions with a smaller ℓ0 norm than ℓ2 (i.e., with a lesser number of changes with respect to the original table). However the optimization problem formulated with ℓ2 requires half the number of variables than that for ℓ1, and it is more efficiently solved. In this work a pseudo-Huber function (which is a continuous nonlinear approximation of the Huber function) is considered to measure the distance between the original and protected tables. This pseudo-Huber function approximates ℓ1 but can be formulated with the same number of variables than ℓ2. It results in a nonlinear convex optimization problem which, theoretically, can be solved in polynomial time. Some preliminary results using the Huber-CTA model are reported.
Supported by grants MTM2012-31440 of the Spanish Ministry of Economy and Competitiveness, SGR-2014-542 of the Government of Catalonia, and DwB INFRA-2010-262608 of the FP7 European Union Program.
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Castro, J. (2014). A CTA Model Based on the Huber Function. In: Domingo-Ferrer, J. (eds) Privacy in Statistical Databases. PSD 2014. Lecture Notes in Computer Science, vol 8744. Springer, Cham. https://doi.org/10.1007/978-3-319-11257-2_7
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DOI: https://doi.org/10.1007/978-3-319-11257-2_7
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