Before releasing databases which contain sensitive information about individuals, statistical
agencies have to apply Statistical Disclosure Limitation (SDL) methods to such data. The goal
of these methods is to minimize the risk of disclosure of the confidential information and at the
same time provide legitimate data users with accurate information about the population of interest.
SDL methods applicable to the microdata (i.e. collection of individual records) are often called
masking methods. In this paper, several multiplicative noise masking schemes are presented.
These schemes are designed to preserve positivity and inequality constraints in the data together
with the vector of means and covariance matrix.
CitationOganian, Anna. Multiplicative noise for masking numerical microdata with constraints. "SORT", Special Issue, p. 99-112.
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