Perturbation of Numerical Confidential Data via Skew-t Distributions

Seokho Lee (), Marc G. Genton () and Reinaldo B. Arellano-Valle ()
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Seokho Lee: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115
Marc G. Genton: Department of Statistics, Texas A& M University, College Station, Texas 77843
Reinaldo B. Arellano-Valle: Departamento de Estadística, Facultad de Matemática, Pontificia Universidad Católica de Chile, Santiago 22, Chile

Management Science, 2010, vol. 56, issue 2, 318-333

Abstract: We propose a new data perturbation method for numerical database security problems based on skew-t distributions. Unlike the normal distribution, the more general class of skew-t distributions is a flexible parametric multivariate family that can model skewness and heavy tails in the data. Because databases having a normal distribution are seldom encountered in practice, the newly proposed approach, coined the skew-t data perturbation (STDP) method, is of great interest for database managers. We also discuss how to preserve the sample mean vector and sample covariance matrix exactly for any data perturbation method. We investigate the performance of the STDP method by means of a Monte Carlo simulation study and compare it with other existing perturbation methods. Of particular importance is the ability of STDP to reproduce characteristics of the joint tails of the distribution in order for database users to answer higher-level questions. We apply the STDP method to a medical database related to breast cancer.

Keywords: confidentiality; database management; kurtosis; multivariate; security; simulation; skewness (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:56:y:2010:i:2:p:318-333

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