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Data Shuffling--A New Masking Approach for Numerical Data

Author

Listed:
  • Krishnamurty Muralidhar

    (Gatton College of Business and Economics, University of Kentucky, Lexington, Kentucky 40506)

  • Rathindra Sarathy

    (Spears School of Business, Oklahoma State University, Stillwater, Oklahoma 74078)

Abstract

This study discusses a new procedure for masking confidential numerical data--a procedure called data shuffling--in which the values of the confidential variables are "shuffled" among observations. The shuffled data provides a high level of data utility and minimizes the risk of disclosure. From a practical perspective, data shuffling overcomes reservations about using perturbed or modified confidential data because it retains all the desirable properties of perturbation methods and performs better than other masking techniques in both data utility and disclosure risk. In addition, data shuffling can be implemented using only rank-order data, and thus provides a nonparametric method for masking. We illustrate the applicability of data shuffling for small and large data sets.

Suggested Citation

  • Krishnamurty Muralidhar & Rathindra Sarathy, 2006. "Data Shuffling--A New Masking Approach for Numerical Data," Management Science, INFORMS, vol. 52(5), pages 658-670, May.
    • Handle: RePEc:inm:ormnsc:v:52:y:2006:i:5:p:658-670
    DOI: 10.1287/mnsc.1050.0503
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    References listed on IDEAS

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    1. 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.
    2. Robert T. Clemen & Terence Reilly, 1999. "Correlations and Copulas for Decision and Risk Analysis," Management Science, INFORMS, vol. 45(2), pages 208-224, February.
    3. Ram Gopal & Robert Garfinkel & Paulo Goes, 2002. "Confidentiality via Camouflage: The CVC Approach to Disclosure Limitation When Answering Queries to Databases," Operations Research, INFORMS, vol. 50(3), pages 501-516, June.
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    Citations

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    Cited by:

    1. Alexander Naidenov, 2016. "Contemporary methods for statistical disclosure control," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 125-134.
    2. Castro, Jordi, 2012. "Recent advances in optimization techniques for statistical tabular data protection," European Journal of Operational Research, Elsevier, vol. 216(2), pages 257-269.
    3. Sage, Andrew J. & Wright, Stephen E., 2016. "Obtaining cell counts for contingency tables from rounded conditional frequencies," European Journal of Operational Research, Elsevier, vol. 250(1), pages 91-100.
    4. Natsuki Sano, 2022. "Utility and Risk Evaluation of Synthetic Data by Orthogonal Transformation," The Review of Socionetwork Strategies, Springer, vol. 16(1), pages 71-79, April.
    5. Nigel Melville & Michael McQuaid, 2012. "Research Note ---Generating Shareable Statistical Databases for Business Value: Multiple Imputation with Multimodal Perturbation," Information Systems Research, INFORMS, vol. 23(2), pages 559-574, June.
    6. Lomax, Nik & Loukides, Grigorios, 2021. "Privacy-preserving data publishing through anonymization, statistical disclosure control, and de-identification," OSF Preprints 2fvj7, Center for Open Science.
    7. Chu, Amanda M.Y. & Ip, Chun Yin & Lam, Benson S.Y. & So, Mike K.P., 2022. "Vine copula statistical disclosure control for mixed-type data," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    8. repec:crs:wpidms:m2016-07 is not listed on IDEAS
    9. 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.
    10. Templ, Matthias & Kowarik, Alexander & Meindl, Bernhard, 2015. "Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i04).
    11. 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.
    12. Matthew J. Schneider & Dawn Iacobucci, 2020. "Protecting survey data on a consumer level," Journal of Marketing Analytics, Palgrave Macmillan, vol. 8(1), pages 3-17, March.
    13. Yi Qian & Hui Xie, 2015. "Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases," Management Science, INFORMS, vol. 61(3), pages 520-541, March.
    14. Haibing Lu & Jaideep Vaidya & Vijayalakshmi Atluri & Yingjiu Li, 2015. "Statistical Database Auditing Without Query Denial Threat," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 20-34, February.
    15. Amanda M. Y. Chu & Benson S. Y. Lam & Agnes Tiwari & Mike K. P. So, 2019. "An Empirical Study of Applying Statistical Disclosure Control Methods to Public Health Research," IJERPH, MDPI, vol. 16(22), pages 1-17, November.

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