Distribution-preserving statistical disclosure limitation
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
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.:
- Rathindra Sarathy & Krishnamurty Muralidhar & Rahul Parsa, 2002. "Perturbing Nonnormal Confidential Attributes: The Copula Approach," Management Science, INFORMS, vol. 48(12), pages 1613-1627, December.
- 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.
- 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.
- John M. Abowd & John C. Haltiwanger & Julia I. Lane, 2004. "Integrated Longitudinal Employee-Employer Data for the United States," Longitudinal Employer-Household Dynamics Technical Papers 2004-02, Center for Economic Studies, U.S. Census Bureau.
- John M. Abowd & Bryce E. Stephens & Lars Vilhuber & Fredrik Andersson & Kevin L. McKinney & Marc Roemer & Simon Woodcock, 2009. "The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators," NBER Chapters,in: Producer Dynamics: New Evidence from Micro Data, pages 149-230 National Bureau of Economic Research, Inc.
- 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," Longitudinal Employer-Household Dynamics Technical Papers 2002-05, Center for Economic Studies, U.S. Census Bureau.
- John Abowd & Bryce Stephens & Lars Vilhuber, 2006. "The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators," Longitudinal Employer-Household Dynamics Technical Papers 2006-01, Center for Economic Studies, U.S. Census Bureau.
- Reiter, Jerome P. & Raghunathan, Trivellore E., 2007. "The Multiple Adaptations of Multiple Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1462-1471, December.
- John M. Abowd & Paul A. Lengermann & Kevin L. McKinney, 2002. "The Measurement of Human Capital in the U.S. Economy," Longitudinal Employer-Household Dynamics Technical Papers 2002-09, Center for Economic Studies, U.S. Census Bureau, revised Mar 2003. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:4228-4242. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.