A novel variation of the data swapping approach to statistical disclosure control is presented, aimed particularly at preservation of multivariate relations in the original dataset. A theorem is proved in support of the method, and extensive empirical investigation is reported.

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.