Random orthogonal matrix masking methodology for microdata release

Authors:
Daniel Ting;Stephen E. Fienberg;Mario Trottini
Affiliations:
Department of Statistics, University of California Berkeley, 385 Evans Hall, Berkeley, CA 94720 3860, USA.;Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA.;Department of Statistics and Operations Research, University of Alicante, Apartado de correos 99, 03080 Alicante, Spain
Venue:
International Journal of Information and Computer Security
Year:
2008

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Abstract

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.