Distribution Preserving Statistical Disclosure Limitation
September 2006
Working Paper Number:
tp-2006-04
Abstract
One approach to limiting disclosure risk in public-use microdata is to release multiply-imputed,
partially synthetic data sets. These are data on actual respondents, but with confidential data
replaced by multiply-imputed synthetic values. A mis-specified imputation model can invalidate
inferences because the distribution of synthetic data is completely determined by the model used
to generate them. We present two practical methods of generating synthetic values when the imputer
has only limited information about the true data generating process. One is applicable when
the true likelihood is known up to a monotone transformation. The second requires only limited
knowledge of the true likelihood, but nevertheless preserves the conditional distribution of the confidential
data, up to sampling error, on arbitrary subdomains. Our method maximizes data utility
and minimizes incremental disclosure risk up to posterior uncertainty in the imputation model and
sampling error in the estimated transformation. We validate the approach with a simulation and
application to a large linked employer-employee database.
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:
analysis,
econometric,
estimating,
data,
researcher,
statistical,
report,
microdata,
survey,
statistical agencies,
respondent,
research,
information,
empirical,
longitudinal,
department,
privacy,
record,
population,
census bureau,
aging,
research census,
employee data,
statistical disclosure
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:
Standard Industrial Classification,
Service Annual Survey,
National Science Foundation,
Department of Economics,
Cornell University,
Unemployment Insurance,
Research Data Center,
Alfred P Sloan Foundation,
Longitudinal Employer Household Dynamics,
Cornell Institute for Social and Economic Research,
LEHD Program
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