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Perturbing Nonnormal Confidential Attributes: The Copula Approach

Author

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  • Rathindra Sarathy

    (Department of Management Science and Information Systems, Oklahoma State University, Stillwater, Oklahoma 74078-4011)

  • Krishnamurty Muralidhar

    (School of Management, Gatton College of Business and Economics, University of Kentucky, Lexington, Kentucky 40506-0034)

  • Rahul Parsa

    (College of Business and Public Administration, Drake University, Des Moines, Iowa 50311)

Abstract

Protecting confidential, numerical data in databases from disclosure is an important issue both for commercial organizations as well as data-gathering and disseminating organizations (such as the Census Bureau). Prior studies have shown that perturbation methods are effective in protecting such confidential data from snoopers. Perturbation methods have to provide legitimate users with accurate (unbiased) information, and also provide adequate security against disclosure of confidential information to snoopers. For databases described by nonnormal multivariate distributions, existing perturbation methods do not provide unbiased characteristics. In this study, we develop a copula-based perturbation method capable of maintaining the marginal distribution of perturbed attributes to be the same before and after perturbation. In addition, this method also preserves the rank order correlation between the confidential and nonconfidential attributes, thereby maintaining monotonic relationships between attributes. The method proposed in this study provides a high level of protection against inferential disclosure. An investigation of the new perturbation method for simulated databases shows that the method performs effectively. The methodology presented in this study represents a signicant step toward improving the practical applicability of data perturbation methods.

Suggested Citation

  • Rathindra Sarathy & Krishnamurty Muralidhar & Rahul Parsa, 2002. "Perturbing Nonnormal Confidential Attributes: The Copula Approach," Management Science, INFORMS, vol. 48(12), pages 1613-1627, December.
    • Handle: RePEc:inm:ormnsc:v:48:y:2002:i:12:p:1613-1627
    DOI: 10.1287/mnsc.48.12.1613.439
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    References listed on IDEAS

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    1. Krishnamurty Muralidhar & Dinesh Batra & Peeter J. Kirs, 1995. "Accessibility, Security, and Accuracy in Statistical Databases: The Case for the Multiplicative Fixed Data Perturbation Approach," Management Science, INFORMS, vol. 41(9), pages 1549-1564, September.
    2. 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.
    3. Robert T. Clemen & Terence Reilly, 1999. "Correlations and Copulas for Decision and Risk Analysis," Management Science, INFORMS, vol. 45(2), pages 208-224, February.
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    1. Mario Trottini, 2008. "Data disclosure limitation as a decision problem," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 109-134.
    2. Woodcock, Simon D. & Benedetto, Gary, 2009. "Distribution-preserving statistical disclosure limitation," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4228-4242, October.
    3. Syam Menon & Sumit Sarkar & Shibnath Mukherjee, 2005. "Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns," Information Systems Research, INFORMS, vol. 16(3), pages 256-270, September.
    4. Yi Qian & Hui Xie, 2013. "Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases," NBER Working Papers 19586, National Bureau of Economic Research, Inc.
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    6. 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).
    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.
    8. 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.
    9. 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.
    10. 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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