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Use of an Audit Program to Improve Confidentiality Protection of Tabular Data at the Bureau of Labor Statistics

Randall K. Powers and Stephen H. Cohen


U.S. Statistical Agencies develop disclosure avoidance processes to ensure that individually identifiable data can not be detected in the published tabular tables from confidential data. The Disclosure Audit System (DAS) is software that uses linear programming methodologies to audit the effectiveness of disclosure avoidance processes to prevent identification of individual responses. Currently, all data released by programs at the Bureau of Labor Statistics are subject to heuristic disclosure analysis algorithms which ensure that data users outside the Bureau can't ascertain the values of individually respondent data. The Covered Employment and Wages Survey program publishes quarterly and annual counts of employment and wages reported by employers covering 98% of U.S. jobs, available at the national, state, MSA, and county levels by North American Industry Classification System (NAICS) codes. This paper will evaluate the effectiveness of confidentiality procedures used to protect the confidentiality of tabular data in the Covered Employment and Wages Survey. We will discuss how we applied the DAS software to evaluate these confidentiality procedures, and the ramifications of our findings.