Dynamically Consistent Noise Infusion and Partially Synthetic Data as Confidentiality Protection Measures for Related Time Series
July 2012
Working Paper Number:
CES-12-13
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.
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payroll,
statistical,
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disclosure,
confidentiality,
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earnings,
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Center for Economic Studies,
County Business Patterns,
Employer Identification Numbers,
National Research Council,
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Journal of Economic Literature,
Research Data Center,
North American Industry Classification System,
Alfred P Sloan Foundation,
Longitudinal Employer Household Dynamics,
Cornell Institute for Social and Economic Research,
LEHD Program,
Quarterly Workforce Indicators,
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Census Bureau Disclosure Review Board,
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