Masking methods for the safe dissemination of microdata consist of distorting the original data while
preserving a pre-defined set of statistical properties in the microdata. For continuous variables,
available methodologies rely essentially on matrix masking and in particular on adding noise to the
original values, using more or less refined procedures depending on the extent of information that one
seeks to preserve. Almost all of these methods make use of the critical assumption that the original
datasets follow a normal distribution and/or that the noise has such a distribution. This assumption is,
however, restrictive in the sense that few variables follow empirically a Gaussian pattern: the
distribution of household income, for example, is positively skewed, and this skewness is essential
information that has to be considered and preserved. This paper addresses these issues by presenting a
simple multiplicative masking method that preserves skewness of the original data while offering a
sufficient level of disclosure risk control. Numerical examples are provided, leading to the suggestion
that this method could be well-suited for the dissemination of a broad range of microdata, including
those based on administrative and business records.
