  Statistical disclosure limitation (SDL) methods aim to provide analysts
general access to a data set while limiting the risk of disclosure of
individual records. Many methods in the existing literature are aimed only at
the case of univariate distributions, but the multivariate case is crucial,
since most statistical analyses are multivariate in nature. Yet preserving the
multivariate structure of the data can be challenging, especially when both
continuous and categorical variables are present. Here we present a new SDL
method that automatically attains the correct multivariate structure,
regardless of whether the data are continuous, categorical or mixed. In
addition, operational methods for assessing data quality and risk will be
explored.
