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Distribution-Preserving Statistical Disclosure Limitation

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

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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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File URL: http://www.econ.sfu.ca/research/RePEc/sfu/sfudps/dp07-15.pdf
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Publisher Info
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
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Handle: RePEc:sfu:sfudps:dp07-15

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Postal: Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
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Web page: http://www.econ.sfu.ca/
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Postal: Working Paper Coordinator, Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
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Related research
Keywords: statistical disclosure limitation confidentiality privacy multiple imputation partially synthetic data

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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

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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.:
  1. 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)
  2. John M. 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!] (restricted)
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This page was last updated on 2008-10-10.


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