A theoretical basis for perturbation methods
Statistics and Computing
Disclosure risk assessment in statistical microdata protection via advanced record linkage
Statistics and Computing
Perturbing Nonnormal Confidential Attributes: The Copula Approach
Management Science
Synthesizing: art of anonymization
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
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One approach to limiting disclosure risk in public-use microdata is to release multiply-imputed, partially synthetic data sets. These are data on actual respondents, but with confidential data replaced by multiply-imputed synthetic values. A mis-specified imputation model can invalidate inferences based on the partially synthetic data, because the imputation model determines the distribution of synthetic values. We present a practical method to generate synthetic values when the imputer has only limited information about the true data generating process. We combine a simple imputation model (such as regression) with density-based transformations that preserve the distribution of the confidential data, up to sampling error, on specified subdomains. We demonstrate through simulations and a large scale application that our approach preserves important statistical properties of the confidential data, including higher moments, with low disclosure risk.