Suchergebnisse

When releasing microdata to the public, data disseminators typically alter the original data to protect the confidentiality of database subjects' identities and sensitive attributes. However, such alteration negatively impacts the utility (quality) of the released data. In this paper, we present quantitative measures of data utility for masked microdata, with the aim of improving disseminators' evaluations of competing masking strategies. The measures, which are global in that they reflect similarities between the entire distributions of the original and released data, utilize empirical distribution estimation, cluster analysis, and propensity scores. We evaluate the measures using both simulated and genuine data. The results suggest that measures based on propensity score methods are the most promising for general use.

Abstract
The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly to the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This article focuses on some of the key research findings of the eight nodes, organized into six topics: (1) improving census and survey data-quality and data collection methods; (2) using alternative sources of data; (3) protecting privacy and confidentiality by improving disclosure avoidance; (4) using spatial and spatio-temporal statistical modeling to improve estimates; (5) assessing data cost and data-quality tradeoffs; and (6) combining information from multiple sources. The article concludes with an evaluation of the ability of the FSS to apply the NCRN's research outcomes, suggests some next steps, and discusses the implications of this research-network model for future federal government research initiatives.