Distribution-Preserving Statistical Disclosure Limitation

Simon Woodcock and Gary Benedetto

MPRA Paper from University Library of Munich, Germany

Abstract: 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 because the distribution of synthetic data is completely determined by the model used to generate them. We present two practical methods of generating synthetic values when the imputer has only limited information about the true data generating process. One is applicable when the true likelihood is known up to a monotone transformation. The second requires only limited knowledge of the true likelihood, but nevertheless preserves the conditional distribution of the confidential data, up to sampling error, on arbitrary subdomains. Our method maximizes data utility and minimizes incremental disclosure risk up to posterior uncertainty in the imputation model and sampling error in the estimated transformation. We validate the approach with a simulation and application to a large linked employer-employee database.

Keywords: statistical disclosure limitation; confidentiality; privacy; multiple imputation; partially synthetic data (search for similar items in EconPapers)
JEL-codes: C4 C81 (search for similar items in EconPapers)
Date: 2006-09
New Economics Papers: this item is included in nep-ecm
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https://mpra.ub.uni-muenchen.de/155/1/MPRA_paper_155.pdf original version (application/pdf)

Related works:
Journal Article: Distribution-preserving statistical disclosure limitation (2009)
Working Paper: Distribution-Preserving Statistical Disclosure Limitation (2007)
Working Paper: Distribution Preserving Statistical Disclosure Limitation (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:155

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