Maximum entropy simulation for microdata protection
Statistics and Computing
A theoretical basis for perturbation methods
Statistics and Computing
A rejoinder to the comments by Polettini and Stander
Statistics and Computing
Secure and useful data sharing
Decision Support Systems
Dare to share: Protecting sensitive knowledge with data sanitization
Decision Support Systems
Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns
Information Systems Research
Data ShufflingA New Masking Approach for Numerical Data
Management Science
A polynomial-time approximation to optimal multivariate microaggregation
Computers & Mathematics with Applications
Random orthogonal matrix masking methodology for microdata release
International Journal of Information and Computer Security
Distribution-preserving statistical disclosure limitation
Computational Statistics & Data Analysis
Perturbation of Numerical Confidential Data via Skew-t Distributions
Management Science
Why swap when you can shuffle? a comparison of the proximity swap and data shuffle for numeric data
PSD'06 Proceedings of the 2006 CENEX-SDC project international conference on Privacy in Statistical Databases
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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.