  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.
