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Dynamically Consistent Noise Infusion and Partially Synthetic Data as Confidentiality Protection Measures for Related Time Series

Author

Listed:
  • John M. Abowd
  • Kaj Gittings
  • Kevin L. McKinney
  • Bryce E. Stephens
  • Lars Vilhuber
  • Simon Woodcock

Abstract

The Census Bureau's Quarterly Workforce Indicators (QWI) provide detailed quarterly statistics on employment measures such as worker and job flows, tabulated by worker characteristics in various combinations. The data are released for several levels of NAICS industries and geography, the lowest aggregation of the latter being counties. Disclosure avoidance methods are required to protect the information about individuals and businesses that contribute to the underlying data. The QWI disclosure avoidance mechanism we describe here relies heavily on the use of noise infusion through a permanent multiplicative noise distortion factor, used for magnitudes, counts, differences and ratios. There is minimal suppression and no complementary suppressions. To our knowledge, the release in 2003 of the QWI was the first large-scale use of noise infusion in any official statistical product. We show that the released statistics are analytically valid along several critical dimensions { measures are unbiased and time series properties are preserved. We provide an analysis of the degree to which confidentiality is protected. Furthermore, we show how the judicious use of synthetic data, injected into the tabulation process, can completely eliminate suppressions, maintain analytical validity, and increase the protection of the underlying confidential data.

Suggested Citation

  • 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.
  • Handle: RePEc:cen:wpaper:12-13
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    File URL: https://www2.census.gov/ces/wp/2012/CES-WP-12-13.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. John M. Abowd & Julia I. Lane, 2004. "New Approaches to Confidentiality Protection Synthetic Data, Remote Access and Research Data Centers," Longitudinal Employer-Household Dynamics Technical Papers 2004-03, Center for Economic Studies, U.S. Census Bureau.
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    Citations

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    Cited by:

    1. Kevin L. McKinney & Andrew S. Green & Lars Vilhuber & John M. Abowd, 2020. "Total Error and Variability Measures for the Quarterly Workforce Indicators and LEHD Origin Destination Employment Statistics in OnTheMap," Working Papers 20-30, Center for Economic Studies, U.S. Census Bureau.
    2. Miranda, Javier & Lars Vilhuber, 2014. "Looking Back On Three Years Of Using The Synthetic Lbd Beta," Working Papers 14-11, Center for Economic Studies, U.S. Census Bureau.
    3. Kevin L. McKinney & Andrew S. Green & Lars Vilhuber & John M. Abowd, 2017. "Total Error and Variability Measures with Integrated Disclosure Limitation for Quarterly Workforce Indicators and LEHD Origin Destination Employment Statistics in On The Map," Working Papers 17-71, Center for Economic Studies, U.S. Census Bureau.
    4. John M. Abowd & Kevin L. McKinney, 2014. "Noise Infusion As A Confidentiality Protection Measure For Graph-Based Statistics," Working Papers 14-30, Center for Economic Studies, U.S. Census Bureau.
    5. John M. Abowd & Ian M. Schmutte & Lars Vilhuber, 2018. "Disclosure Limitation and Confidentiality Protection in Linked Data," Working Papers 18-07, Center for Economic Studies, U.S. Census Bureau.
    6. 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.
    7. Thiemo Fetzer, 2014. "Fracking Growth," CEP Discussion Papers dp1278, Centre for Economic Performance, LSE.
    8. Robert Manduca, 2018. "The US Census Longitudinal Employer-Household Dynamics Datasets," REGION, European Regional Science Association, vol. 5, pages 5-12.

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    More about this item

    Keywords

    noise infusion; synthetic data; statistical disclosure limitation; time-series; local labor markets; gross job flows; gross worker flows; confidentiality protection;
    All these keywords.

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • J40 - Labor and Demographic Economics - - Particular Labor Markets - - - General

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