Perturbing Nonnormal Confidential Attributes: The Copula Approach

Rathindra Sarathy (), Krishnamurty Muralidhar () and Rahul Parsa ()
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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

Management Science, 2002, vol. 48, issue 12, 1613-1627

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.

Keywords: database management; data security; data perturbation; privacy and confidentiality; copulas (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (12)

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