Marginality: a numerical mapping for enhanced treatment of nominal and
  hierarchical attributes
  The purpose of statistical disclosure control (SDC) of microdata, a.k.a. data
anonymization or privacy-preserving data mining, is to publish data sets
containing the answers of individual respondents in such a way that the
respondents corresponding to the released records cannot be re-identified and
the released data are analytically useful. SDC methods are either based on
masking the original data, generating synthetic versions of them or creating
hybrid versions by combining original and synthetic data. The choice of SDC
methods for categorical data, especially nominal data, is much smaller than the
choice of methods for numerical data. We mitigate this problem by introducing a
numerical mapping for hierarchical nominal data which allows computing means,
variances and covariances on them.
