Perturbation of numerical confidential data via skew-t distributions

Seokho Lee*, Marc Genton, Reinaldo B. Arellano-Valle

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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 fiexible 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.

Original languageEnglish (US)
Pages (from-to)318-333
Number of pages16
JournalManagement Science
Volume56
Issue number2
DOIs
StatePublished - Feb 1 2010

Keywords

  • Confidentiality
  • Database management
  • Kurtosis
  • Multivariate
  • Security
  • Simulation
  • Skewness

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research

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