On disclosure protection for non-traditional statistical outputs: kernel density estimators

This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.

In this paper we discuss a specific component of a research agenda aimed at disclosure protections for "non-traditional" statistical outputs. We argue that these outputs present different disclosure risks than normally faced and hence may require new thinking on the issue. Specifically, we argue that kernel density estimators, while powerful (high quality) descriptions of cross-sections, pose potential disclosure risks that depend materially on the selection of bandwidth. We illustrate these risks using a unique, non-confidential data set on the statistical universe of coal mines and present potential solutions. Finally, we discuss current practices at the U.S. Census Bureau's Center for Economic Studies for performing disclosure analysis on kernel density estimators.