Statistical disclosure control for continuous variables using an extended skew-t copula

Man Ying Amanda CHU, Chun Yin IP, Benson S. Y. LAM, Mike K. P. SO

Research output: Contribution to journalArticlespeer-review

1 Citation (Scopus)

Abstract

In this article, we extend the skew-t data perturbation (STDP) to develop a new statistical disclosure control (SDC) method for data with continuous variables. In this new SDC method, we construct an extended skew-t (EST) copula to release confidential data for third-party usage. Using the EST copula for producing perturbed data, we can incorporate rich statistical information in the perturbed data while preserving the marginal distributions of the data. An advancement of this EST-SDC method is to use a copula distribution, which allows generation of perturbed data from bivariate conditional EST copulas sequentially. We discuss the methodology of EST-SDC and outline some statistical properties derived from copula theories. Simulations and a real data study are included to demonstrate how the EST-SDC method can be applied and to compare with the STDP method. Copyright © 2021 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)96-115
JournalApplied Stochastic Models in Business and Industry
Volume38
Issue number1
Early online date06 Oct 2021
DOIs
Publication statusPublished - Jan 2022

Citation

Chu, A. M. Y., Ip, C. Y., Lam, B. S. Y., & So, M. K. P. (2022). Statistical disclosure control for continuous variables using an extended skew-t copula. Applied Stochastic Models in Business and Industry, 38(1), 96-115. doi: 10.1002/asmb.2650

Keywords

  • Business analytics
  • Confidentiality
  • Copula
  • Data privacy
  • Sensitive data

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