Abstract
The papers in this section of the June Statistical Journal of the IAOS are an outgrowth of a project supported by the National Science Foundation (US-NSF), Census Bureau (US-Census), Internal Revenue Service (US-IRS), and Institut fur Arbeitsmarktund Berufsforschung (DE-IAB) to promote collaboration between researchers at the statistical agencies (Census and IAB) and at Cornell, Duke, Michigan and Simon Fraser universities.1 The goal of the research projects was to explore the potential for using synthetic data methods in the manner originally suggested by Little (1993) and Rubin (1993; see citation in Jarmin et al., this section) for the protection and release of micro-data from establishment censuses, linked employer-employee administrative records, linked survey-administrative record data, large scale surveys, and household censuses. Summaries of the papers from this project were presented at the NSF-Census-IRS Workshop on Synthetic Data and Confidentiality Protection (2009).2
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