Distribution-Preserving Statistical Disclosure Limitation

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Distribution-Preserving Statistical Disclosure Limitation

Author Info

Simon D. Woodcock () (Simon Fraser University)
Gary Benedetto () (US Census Bureau)

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Simon D. Woodcock

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 con dential data replaced by multiply-imputed synthetic values. When imputing confidential values, a mis-specified model can invalidate inferences, because the distribution of synthetic data is determined by the model used to generate them. 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 a series of density-based transformations to pre- serve the distribution of the con dential data, up to sampling error, on speci ed subdomains. We demonstrate through simulation and a large scale application that our approach preserves important statistical properties of the con dential data, including higher moments, with low disclosure risk.

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Paper provided by Department of Economics, Simon Fraser University in its series Discussion Papers with number dp07-15.

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Length: 39
Date of creation: Sep 2007
Date of revision:
Handle: RePEc:sfu:sfudps:dp07-15

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Other versions of this item:

Article
- Woodcock, Simon D. & Benedetto, Gary, 2009. "Distribution-preserving statistical disclosure limitation," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4228-4242, October. [Downloadable!] (restricted)
Paper
- Woodcock, Simon & Benedetto, Gary, 2006. "Distribution-Preserving Statistical Disclosure Limitation," MPRA Paper 155, University Library of Munich, Germany. [Downloadable!]
- Simon D. Woodcock & Gary Benedetto, 2006. "Distribution Preserving Statistical Disclosure Limitation," Technical Papers 2006-04, Longitudinal Employer-Household Dynamics, Center for Economic Studies, U.S. Census Bureau. [Downloadable!]

Find related papers by JEL classification:
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General
C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
C5 - Mathematical and Quantitative Methods - - Econometric Modeling

This paper has been announced in the following NEP Reports:

NEP-ALL-2007-09-30 (All new papers)
NEP-ECM-2007-09-30 (Econometrics)

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

Reiter, Jerome P., 2005. "Estimating Risks of Identification Disclosure in Microdata," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1103-1112, December. [Downloadable!] (restricted)
John J. Abowd & John Haltiwanger & Julia Lane, 2004. "Integrated Longitudinal Employer-Employee Data for the United States," American Economic Review, American Economic Association, vol. 94(2), pages 224-229, May. [Downloadable!]
Other versions:
- John M. Abowd & John C. Haltiwanger & Julia I. Lane, 2004. "Integrated Longitudinal Employee-Employer Data for the United States," Technical Papers 2004-02, Longitudinal Employer-Household Dynamics, Center for Economic Studies, U.S. Census Bureau. [Downloadable!]
John Abowd & Bryce Stephens & Lars Vilhuber, 2006. "The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators," Technical Papers 2006-01, Longitudinal Employer-Household Dynamics, Center for Economic Studies, U.S. Census Bureau. [Downloadable!]
Other versions:
- John M. Abowd & Bryce E. Stephens & Lars Vilhuber & Fredrik Andersson & Kevin L. McKinney & Marc Roemer & Simon Woodcock, 2002. "The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators," Technical Papers 2002-05, Longitudinal Employer-Household Dynamics, Center for Economic Studies, U.S. Census Bureau. [Downloadable!]

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