Statistical agencies frequently publish microdata that have been altered to protect confidentiality. Such data retain utility for many types of broad analyses but can yield biased or insufficiently precise results in others. Research access to de-identified versions of the restricted-use data...

Data sharing and access are venerable problems embedded in a rapidly changing milieu. Pressure points include the increasingly data-driven nature of science, the volume, complexity, and distributed nature of data, new concerns regarding privacy and confidentiality, and rising attention to...

Researchers apply sampling weights to take account of unequal sample selection probabilities and to frame coverage errors and nonresponses. If researchers do not weight when appropriate, they risk having biased estimates. Alternatively, when they unnecessarily apply weights, they can create an...

In most countries, national statistical agencies do not release establishment-level business microdata, because doing so represents too large a risk to establishments' confidentiality. One approach with the potential for overcoming these risks is to release synthetic data; that is, the released...

In the U.S. Census of Manufactures, the Census Bureau imputes missing values using a combination of mean imputation, ratio imputation, and conditional mean imputation. It is well-known that imputations based on these methods can result in underestimation of variability and potential bias in...

Federal statistics agencies strive to release data products that are informative for many purposes, yet also protect the privacy and confidentiality of data subjects’ identities and sensitive attributes. This article reviews the role that differential privacy, a disclosure risk criterion...