Disclosure Limitation and Confidentiality Protection in Linked Data
January 2018
Working Paper Number:
CES-18-07
Abstract
Confidentiality protection for linked administrative data is a combination of access modalities and statistical disclosure limitation. We review traditional statistical disclosure limitation methods and newer methods based on synthetic data, input noise infusion and formal privacy. We discuss how these methods are integrated with access modalities by providing three detailed examples. The first example is the linkages in the Health and Retirement Study to Social Security Administration data. The second example is the linkage of the Survey of Income and Program Participation to administrative data from the Internal Revenue Service and the Social Security Administration. The third example is the Longitudinal Employer-Household Dynamics data, which links state unemployment insurance records for workers and firms to a wide variety of censuses and surveys at the U.S. Census Bureau. For examples, we discuss access modalities, disclosure limitation methods, the effectiveness of those methods, and the resulting analytical validity. The final sections discuss recent advances in access modalities for linked administrative data.
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:
data,
statistical,
database,
census data,
microdata,
survey,
disclosure,
agency,
respondent,
confidentiality,
linked census,
information,
statistician,
privacy,
record,
employment statistics,
irs,
ssa,
filing,
employee data,
datasets,
statistical disclosure
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several tasks, including entity tagging.
The model is able to label words and phrases by part-of-speech,
including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are
identified to contain references to specific institutions, datasets, and other organizations.
:
American Economic Association,
Internal Revenue Service,
Standard Industrial Classification,
Bureau of Labor Statistics,
Social Security Administration,
Service Annual Survey,
American Statistical Association,
National Science Foundation,
Center for Economic Studies,
Stern School of Business,
County Business Patterns,
Federal Reserve Bank,
Statistics Canada,
National Longitudinal Survey of Youth,
Department of Economics,
Chicago Census Research Data Center,
Survey of Income and Program Participation,
Cornell University,
Social Security,
Unemployment Insurance,
Research Data Center,
North American Industry Classification System,
American Community Survey,
Social Security Number,
Health and Retirement Study,
National Institute on Aging,
Alfred P Sloan Foundation,
Longitudinal Employer Household Dynamics,
Detailed Earnings Records,
Summary Earnings Records,
Federal Insurance Contribution Act,
Sloan Foundation,
National Center for Health Statistics,
Quarterly Workforce Indicators,
Medicaid Services,
Centers for Medicare,
European Union,
National Institutes of Health,
Quarterly Census of Employment and Wages,
University of Michigan,
Census Bureau Disclosure Review Board,
Commodity Flow Survey,
United Nations,
Disclosure Review Board,
Business Dynamics Statistics,
Federal Statistical Research Data Center
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