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Multiply-Imputing Confidential Characteristics and File Links in Longitudinal Linked Data

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  • John M. Abowd
  • Simon D. Woodcock

Abstract

This paper describes ongoing research to protect confidentiality in longitudinal linked data through creation of multiply-imputed, partially synthetic data. We present two enhancements to the methods of [2]. The first is designed to preserve marginal distributions in the partially synthetic data. The second is designed to protect confidential links between sampling frames.

Suggested Citation

  • John M. Abowd & Simon D. Woodcock, 2004. "Multiply-Imputing Confidential Characteristics and File Links in Longitudinal Linked Data," Longitudinal Employer-Household Dynamics Technical Papers 2004-04, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:tpaper:2004-04
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    File URL: https://www2.census.gov/ces/tp/tp-2004-04.pdf
    File Function: First version, 2004
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    Cited by:

    1. Ainara González de San Román & Yolanda F. Rebollo‐Sanz, 2018. "An Estimation Of Worker And Firm Effects With Censored Data," Bulletin of Economic Research, Wiley Blackwell, vol. 70(4), pages 459-482, October.
    2. Satkartar K. Kinney & Jerome P. Reiter & Javier Miranda, 2014. "Improving The Synthetic Longitudinal Business Database," Working Papers 14-12, Center for Economic Studies, U.S. Census Bureau.
    3. Reiter, Jerome P. & Oganian, Anna & Karr, Alan F., 2009. "Verification servers: Enabling analysts to assess the quality of inferences from public use data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1475-1482, February.
    4. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    5. Reiter, Jerome P., 2008. "Selecting the number of imputed datasets when using multiple imputation for missing data and disclosure limitation," Statistics & Probability Letters, Elsevier, vol. 78(1), pages 15-20, January.
    6. Nowok, Beata & Raab, Gillian M. & Dibben, Chris, 2016. "synthpop: Bespoke Creation of Synthetic Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i11).

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