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Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study

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  • Jerome P. Reiter

Abstract

Summary. The paper presents an illustration and empirical study of releasing multiply imputed, fully synthetic public use microdata. Simulations based on data from the US Current Population Survey are used to evaluate the potential validity of inferences based on fully synthetic data for a variety of descriptive and analytic estimands, to assess the degree of protection of confidentiality that is afforded by fully synthetic data and to illustrate the specification of synthetic data imputation models. Benefits and limitations of releasing fully synthetic data sets are discussed.

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  • Jerome P. Reiter, 2005. "Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 185-205, January.
    • Handle: RePEc:bla:jorssa:v:168:y:2005:i:1:p:185-205
    DOI: 10.1111/j.1467-985X.2004.00343.x
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    Cited by:

    1. Martin Klein & Ricardo Moura & Bimal Sinha, 2021. "Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 273-287, May.
    2. Joshua Snoke & Gillian M. Raab & Beata Nowok & Chris Dibben & Aleksandra Slavkovic, 2018. "General and specific utility measures for synthetic data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 663-688, June.
    3. Woodcock, Simon D. & Benedetto, Gary, 2009. "Distribution-preserving statistical disclosure limitation," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4228-4242, October.
    4. Andrés F. Barrientos & Alexander Bolton & Tom Balmat & Jerome P. Reiter & John M. de Figueiredo & Ashwin Machanavajjhala & Yan Chen & Charles Kneifel & Mark DeLong, 2017. "A Framework for Sharing Confidential Research Data, Applied to Investigating Differential Pay by Race in the U. S. Government," NBER Working Papers 23534, National Bureau of Economic Research, Inc.
    5. Chu, Amanda M.Y. & Ip, Chun Yin & Lam, Benson S.Y. & So, Mike K.P., 2022. "Vine copula statistical disclosure control for mixed-type data," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    6. 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.
    7. Joseph W. Sakshaug & Trivellore E. Raghunathan, 2014. "Generating synthetic microdata to estimate small area statistics in the American Community Survey," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(3), pages 341-368, June.
    8. Yi Qian & Hui Xie, 2013. "Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases," NBER Working Papers 19586, National Bureau of Economic Research, Inc.
    9. Christine N. Kohnen & Jerome P. Reiter, 2009. "Multiple imputation for combining confidential data owned by two agencies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 511-528, April.
    10. 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.
    11. Robbins Michael W., 2014. "The Utility of Nonparametric Transformations for Imputation of Survey Data," Journal of Official Statistics, Sciendo, vol. 30(4), pages 1-26, December.
    12. Joseph W. Sakshaug & Trivellore E. Raghunathan, 2014. "Generating synthetic data to produce public-use microdata for small geographic areas based on complex sample survey data with application to the National Health Interview Survey," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(10), pages 2103-2122, October.
    13. Harrison Quick & Scott H. Holan & Christopher K. Wikle, 2018. "Generating partially synthetic geocoded public use data with decreased disclosure risk by using differential smoothing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 649-661, June.
    14. Goldstein Harvey & Shlomo Natalie, 2020. "A Probabilistic Procedure for Anonymisation, for Assessing the Risk of Re-identification and for the Analysis of Perturbed Data Sets," Journal of Official Statistics, Sciendo, vol. 36(1), pages 89-115, March.
    15. Klein Martin & Sinha Bimal, 2013. "Statistical Analysis of Noise-Multiplied Data Using Multiple Imputation," Journal of Official Statistics, Sciendo, vol. 29(3), pages 425-465, June.
    16. Loong Bronwyn & Rubin Donald B., 2017. "Multiply-Imputed Synthetic Data: Advice to the Imputer," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1005-1019, December.
    17. Jerome P. Reiter, 2009. "Using Multiple Imputation to Integrate and Disseminate Confidential Microdata," International Statistical Review, International Statistical Institute, vol. 77(2), pages 179-195, August.
    18. Myron Gutmann & Kristine Witkowski & Corey Colyer & JoAnne O’Rourke & James McNally, 2008. "Providing Spatial Data for Secondary Analysis: Issues and Current Practices Relating to Confidentiality," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 27(6), pages 639-665, December.
    19. Hang J. Kim & Jerome P. Reiter & Alan F. Karr, 2018. "Simultaneous edit-imputation and disclosure limitation for business establishment data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 63-82, January.
    20. Nigel Melville & Michael McQuaid, 2012. "Research Note ---Generating Shareable Statistical Databases for Business Value: Multiple Imputation with Multimodal Perturbation," Information Systems Research, INFORMS, vol. 23(2), pages 559-574, June.
    21. Sonya Vartivarian & John L. Czajka & Michael Weber, "undated". "Measuring Disclosure Risk and an Examination of the Possibilities of Using Synthetic Data in the Individual Income Tax Return Public Use File," Mathematica Policy Research Reports ab85aed60a3e429786cfcbfdc, Mathematica Policy Research.
    22. Yi Qian & Hui Xie, 2015. "Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases," Management Science, INFORMS, vol. 61(3), pages 520-541, March.
    23. repec:mpr:mprres:5634 is not listed on IDEAS
    24. Dong Hua & Meeden Glen, 2016. "Constructing Synthetic Samples," Journal of Official Statistics, Sciendo, vol. 32(1), pages 113-127, March.
    25. 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.

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