Sampling with Synthesis: A New Approach for Releasing Public Use Census Microdata

Many statistical agencies disseminate samples of census microdata, i.e., data on individual records, to the public. Before releasing the microdata, agencies typically alter identifying or sensitive values to protect data subjects’ confidentiality, for example by coarsening, perturbing, or swapping data. These standard disclosure limitation techniques distort relationships and distributional features in the original data, especially when applied with high intensity. Furthermore, it can be difficult for analysts of the masked public use data to adjust inferences for the effects of the disclosure limitation. Motivated by these shortcomings, we propose an approach to census microdata dissemination called sampling with synthesis. The basic idea is to replace the identifying or sensitive values in the census with multiple imputations, and release samples from these multiply-imputed populations. We demonstrate that sampling with synthesis can improve the quality of public use data relative to sampling followed ∗Jörg Drechsler is research scientist, Institute for Employment Research, Department for Statistical Methods, Regensburger Straße 104, 90478 Nürnberg, Germany,(e-mail: [email protected]); and Jerome P. Reiter is Mrs. Alexander F. Hehmeyer Associate Professor of Statistical Science, Duke University, Durham, NC 27708-0251, (e-mail: [email protected]). This research was supported by a grant from the National Science Foundation (NSF-MMS-0751671).