Abstract
In this paper, we provide a preliminary investigation of t-copulas for perturbing numerical confidential variables. A perturbation approach using Gaussian copulas has been proposed earlier. However, one of the problems with the Gaussian copulas is that it does not preserve tail dependence. In this investigation, we show that the t-copula can be used effectively to provide all the benefits that a Gaussian copula provides and, in addition, maintain tail dependence as well. We illustrate this approach using two examples. We hope to perform a comprehensive investigation of this approach in the future.
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References
Arslan, O.: Family of Multivariate Generalized t Distributions. Journal of Multivariate Analysis 89, 329–337 (2004)
Burridge, J.: Information Preserving Statistical Obfuscation. Statistics and Computing 13, 321–327 (2003)
Clemen, R.T., Reilly, T.: Correlations and Copulas for Decision and Risk Analysis. Management Science 45, 208–224 (1999)
Demarta, S., McNeil, A.J.: The t Copula and Related Copulas. International Statistical Review 73, 111–129 (2005)
Little, R.J.A.: Statistical Analysis of Masked Data. Journal of Official Statistics 9, 407–426 (1993)
Muralidhar, K., Sarathy, R.: A Theoretical Basis for Perturbation Methods. Statistics and Computing 13, 329–335 (2003)
Nelsen, R.B.: An Introduction to Copulas. Springer, New York (1999)
Reiter, J.P.: Simultaneous Use of Multiple Imputation for Missing Data and Disclosure Limitation. Survey Methodology 27, 235–242 (2004)
Rubin, D.B.: Discussion: Statistical Disclosure Limitation. Journal of Official Statistics 9, 462–468 (1993)
Sarathy, R., Muralidhar, K., Parsa, R.: Perturbing Nonnormal Confidential Attributes: The Copula Approach. Management Science 48, 1613–1627 (2002)
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Trottini, M., Muralidhar, K., Sarathy, R. (2008). A Preliminary Investigation of the Impact of Gaussian Versus t-Copula for Data Perturbation. In: Domingo-Ferrer, J., Saygın, Y. (eds) Privacy in Statistical Databases. PSD 2008. Lecture Notes in Computer Science, vol 5262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87471-3_11
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DOI: https://doi.org/10.1007/978-3-540-87471-3_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87470-6
Online ISBN: 978-3-540-87471-3
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