Verification servers: Enabling analysts to assess the quality of inferences from public use data

To protect confidentiality, statistical agencies typically alter data before releasing them to the public. Ideally, although generally not done, the agency also provides a way for secondary data analysts to assess the quality of inferences obtained with the released data. Quality measures can help secondary data analysts to identify inaccurate conclusions resulting from the disclosure limitation procedures, as well as have confidence in accurate conclusions. We propose a framework for an interactive, web-based system that analysts can query for measures of inferential quality. As we illustrate, agencies seeking to build such systems must consider the additional disclosure risks from releasing quality measures. We suggest some avenues of research on limiting these risks. © 2008 Elsevier B.V. All rights reserved.

Duke Scholars

Author Jerome P. Reiter Statistical Science