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Maintaining tail dependence in data shuffling using t copula

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  • Trottini, Mario
  • Muralidhar, Krish
  • Sarathy, Rathindra

Abstract

Data shuffling is a recently proposed technique for masking numerical data where the confidential values are shuffled between records while maintaining all monotonic relationships between the variables in the data set. Data shuffling is based on the multivariate normal copula which assumes that there is no tail dependence in the data set. In many practical situations, however, tail dependence plays a crucial role in decision making. Hence, it is desirable that the data masking procedure be capable of preserving tail dependence when present. In this study, we provide a new data shuffling approach based on t copulas that is capable of maintaining tail dependence in the masked data in a large number of applications.

Suggested Citation

  • Trottini, Mario & Muralidhar, Krish & Sarathy, Rathindra, 2011. "Maintaining tail dependence in data shuffling using t copula," Statistics & Probability Letters, Elsevier, vol. 81(3), pages 420-428, March.
  • Handle: RePEc:eee:stapro:v:81:y:2011:i:3:p:420-428
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    References listed on IDEAS

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    1. Arslan, Olcay, 2004. "Family of multivariate generalized t distributions," Journal of Multivariate Analysis, Elsevier, vol. 89(2), pages 329-337, May.
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    3. Krishnamurty Muralidhar & Rathindra Sarathy, 2006. "Data Shuffling--A New Masking Approach for Numerical Data," Management Science, INFORMS, vol. 52(5), pages 658-670, May.
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    5. Krishnamurty Muralidhar & Rahul Parsa & Rathindra Sarathy, 1999. "A General Additive Data Perturbation Method for Database Security," Management Science, INFORMS, vol. 45(10), pages 1399-1415, October.
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    7. Seokho Lee & Marc G. Genton & Reinaldo B. Arellano-Valle, 2010. "Perturbation of Numerical Confidential Data via Skew-t Distributions," Management Science, INFORMS, vol. 56(2), pages 318-333, February.
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    Cited by:

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    2. Balaev, Alexey, 2014. "The copula based on multivariate t-distribution with vector of degrees of freedom," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 90-110.

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