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The purpose of statistical disclosure control (SDC) of microdata, a.k.a. dataanonymization or privacy-preserving data mining, is to publish data setscontaining the answers of individual respondents in such a way that therespondents corresponding to the released records cannot be re-identified andthe released data are analytically useful. SDC methods are either based onmasking the original data, generating synthetic versions of them or creatinghybrid versions by combining original and synthetic data. The choice of SDCmethods for categorical data, especially nominal data, is much smaller than thechoice of methods for numerical data. We mitigate this problem by introducing anumerical mapping for hierarchical nominal data which allows computing means,variances and covariances on them.

Marginality: a numerical mapping for enhanced treatment of nominal and hierarchical attributes