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

41 Pages Posted: 10 Oct 2012

See all articles by John M. Abowd

John M. Abowd

U.S. Census Bureau; Cornell University Department of Economics; Labor Dynamics Institute; School of Industrial and Labor Relations; NBER (on leave); CREST; IZA Institute of Labor Economics

R. Kaj Gittings

Texas Tech University - Department of Economics

Kevin L McKinney

University of California, Los Angeles (UCLA)

Bryce Stephens

affiliation not provided to SSRN

Lars Vilhuber

Cornell University - Department of Economics; U.S. Census Bureau - Center for Economic Studies

Simon D. Woodcock

Simon Fraser University; Institute for the Study of Labor (IZA)

Date Written: July 1, 2012

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.

Keywords: noise infusion, synthetic data, statistical disclosure limitation, time-series, local labor

JEL Classification: C82, J21, J23, J40

Suggested Citation

Abowd, John Maron and Gittings, R. Kaj and McKinney, Kevin L and Stephens, Bryce and Vilhuber, Lars and Woodcock, Simon D., Dynamically Consistent Noise Infusion and Partially Synthetic Data as Confidentiality Protection Measures for Related Time Series (July 1, 2012). US Census Bureau Center for Economic Studies Paper No. CES-WP-12-13. Available at SSRN: https://ssrn.com/abstract=2159800 or http://dx.doi.org/10.2139/ssrn.2159800

John Maron Abowd (Contact Author)

U.S. Census Bureau ( email )

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Cornell University Department of Economics ( email )

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NBER (on leave) ( email )

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CREST ( email )

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IZA Institute of Labor Economics

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R. Kaj Gittings

Texas Tech University - Department of Economics ( email )

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Kevin L McKinney

University of California, Los Angeles (UCLA) ( email )

405 Hilgard Avenue
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Los Angeles, CA 90095
United States

Bryce Stephens

affiliation not provided to SSRN ( email )

Lars Vilhuber

Cornell University - Department of Economics ( email )

Ithaca, NY
United States

U.S. Census Bureau - Center for Economic Studies ( email )

4700 Silver Hill Road
Washington, DC 20233
United States

Simon D. Woodcock

Simon Fraser University ( email )

Dept. of Economics
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Burnaby, British Columbia V5A 1S6
Canada

HOME PAGE: http://www.sfu.ca/~swoodcoc

Institute for the Study of Labor (IZA) ( email )

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Bonn, D-53072
Germany

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