Vine copula statistical disclosure control for mixed-type data

Amanda M.Y. Chu, Chun Yin Ip, Benson S.Y. Lam and Mike K.P. So

Computational Statistics & Data Analysis, 2022, vol. 176, issue C

Abstract: In this paper, we develop a new statistical disclosure control (SDC) method for mixed-type data based on vine copulas. The use of Gaussian and skew-t copulas has been demonstrated to be capable of incorporating information from the marginal distributions of mixed-type variables, whether they are discrete or continuous. In particular, our proposed SDC method using vine copulas generalizes a data perturbation method using an extended skew-t copula. Our vine-SDC method improves the SDC method using the extended skew-t copula by allowing the bivariate copulas in the vine decomposition to take various forms, thus offering a better fit for the joint distribution of the data and more flexibility in data perturbation. An additional advantage of our vine-SDC method is the significant improvement in computational efficiency compared with that using the extended skew-t copula. We discuss some statistical properties of vine copulas and the methodology of vine-SDC. A simulation and a study of real healthcare survey data are provided to explore the performance and strength of vine-SDC and compare it with a common copula-based SDC method.

Keywords: Confidentiality; Data perturbation; Data privacy; Disclosure risk; Healthcare analytics (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:176:y:2022:i:c:s0167947322001414

DOI: 10.1016/j.csda.2022.107561

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