Title: Random orthogonal matrix masking methodology for microdata release

Authors: Daniel Ting, Stephen E. Fienberg, Mario Trottini

Addresses: 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

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.

Keywords: continuous microdata; data masking; disclosure risk; information loss; multivariate normality; orthogonal transformations; perturbation methods; risk-utility tradeoff; information security; computer security; microdata release; random orthogonal matrix masking; ROMM; data sharing; confidential data; privacy protection; privacy preservation.

DOI: 10.1504/IJICS.2008.016823

International Journal of Information and Computer Security, 2008 Vol.2 No.1, pp.86 - 105

Published online: 24 Jan 2008 *

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