Random orthogonal matrix masking methodology for microdata release. (25th January 2008) - Ting, Daniel; Fienberg, Stephen E.; Trottini, Mario
Statistically defensible methods for disclosure limitation allow users to make inferences about parameters in a model similar to those that would be possible using the original unreleased data. We present a new perturbation method for protecting confidential continuous microdata ? Random Orthogonal Matrix Masking (ROMM) which preserves the sufficient statistics for multivariate normal distributions, and thus is statistically defensible. ROMM encompasses all methods that preserve these statistics and can be restricted to provide 'small' perturbations. We contrast ROMM with other microdata perturbation methods and we discuss methods for evaluating it from the perspective of the tradeoff between disclosure risk and data utility.