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Using Partially Synthetic Microdata to Protect Sensitive Cells in Business Statistics

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  • Javier Miranda
  • Lars Vilhuber

Abstract

We describe and analyze a method that blends records from both observed and synthetic microdata into public-use tabulations on establishment statistics. The resulting tables use synthetic data only in potentially sensitive cells. We describe different algorithms, and present preliminary results when applied to the Census Bureau's Business Dynamics Statistics and Synthetic Longitudinal Business Database, highlighting accuracy and protection afforded by the method when compared to existing public-use tabulations (with suppressions).

Suggested Citation

  • Javier Miranda & Lars Vilhuber, 2016. "Using Partially Synthetic Microdata to Protect Sensitive Cells in Business Statistics," Working Papers 16-10, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:16-10
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    File URL: https://www2.census.gov/ces/wp/2016/CES-WP-16-10.pdf
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    References listed on IDEAS

    as
    1. John Haltiwanger & Ron S. Jarmin & Javier Miranda, 2010. "Who Creates Jobs? Small vs. Large vs. Young," Working Papers 10-17, Center for Economic Studies, U.S. Census Bureau.
    2. Satkartar K. Kinney & Jerome P. Reiter & Arnold P. Reznek & Javier Miranda & Ron S. Jarmin & John M. Abowd, 2011. "Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database," International Statistical Review, International Statistical Institute, vol. 79(3), pages 362-384, December.
    3. Benjamin Wild Pugsley & Ay’egul ahin, 2019. "Grown-up Business Cycles," Review of Financial Studies, Society for Financial Studies, vol. 32(3), pages 1102-1147.
    4. Ron S. Jarmin & Thomas A. Louis & Javier Miranda, 2014. "Expanding The Role Of Synthetic Data At The U.S. Census Bureau," Working Papers 14-10, Center for Economic Studies, U.S. Census Bureau.
    5. 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.
    6. John M. Abowd & Kaj Gittings & Kevin L. McKinney & Bryce E. Stephens & Lars Vilhuber & Simon Woodcock, 2012. "Dynamically Consistent Noise Infusion and Partially Synthetic Data as Confidentiality Protection Measures for Related Time Series," Working Papers 12-13, Center for Economic Studies, U.S. Census Bureau.
    7. Joseph W. Sakshaug & Trivellore E. Raghunathan, 2013. "Synthetic Data For Small Area Estimation In The American Community Survey," Working Papers 13-19, Center for Economic Studies, U.S. Census Bureau.
    8. Drechsler, Jörg & Reiter, Jerome P., 2010. "Sampling With Synthesis: A New Approach for Releasing Public Use Census Microdata," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1347-1357.
    9. 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.
    10. Jörg Drechsler, 2012. "New data dissemination approaches in old Europe -- synthetic datasets for a German establishment survey," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 243-265, April.
    11. Karr, A.F. & Kohnen, C.N. & Oganian, A. & Reiter, J.P. & Sanil, A.P., 2006. "A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality," The American Statistician, American Statistical Association, vol. 60, pages 224-232, August.
    12. Ryan Decker & John Haltiwanger & Ron Jarmin & Javier Miranda, 2014. "The Role of Entrepreneurship in US Job Creation and Economic Dynamism," Journal of Economic Perspectives, American Economic Association, vol. 28(3), pages 3-24, Summer.
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    Cited by:

    1. Daniel H. Weinberg & John M. Abowd & Robert F. Belli & Noel Cressie & David C. Folch & Scott H. Holan & Margaret C. Levenstein & Kristen M. Olson & Jerome P. Reiter & Matthew D. Shapiro & Jolene Smyth, 2017. "Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?," Working Papers 17-59r, Center for Economic Studies, U.S. Census Bureau.
    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.

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