There is a growing demand for public use data while at the same time there are increasing
concerns about the privacy of personal information. One proposed method for …

In order to understand the methods for generating synthetic data, it is important to start with
an understanding of the basis from which these methods have arisen. Viewed purely from …

We present an overview of the evolution of single imputation synthetic datasets in Statistical
Disclosure Control (SDC). Imputation is a widely used technique for generating privacy …

At this year's session in honor of Steve Fienberg's 70th birthday at the Joint Statistical
Meetings in Montréal, I had the the privilege to speak and to reflect on his contributions to …

Synthetic datasets generated within the multiple imputation framework are now commonly
used by statistical agencies to protect the confidentiality of their respondents. More recently …

In data integration contexts, two statistical agencies seek to merge their separate databases
into one file. The agencies also may seek to disseminate data to the public based on the …

To protect the confidentiality of survey respondents' identities and sensitive attributes,
statistical agencies can release data in which confidential values are replaced with multiple …

When seeking to release public use files for confidential data, statistical agencies can
generate fully synthetic data. We propose an approach for making fully synthetic data from …

The book describes different approaches to generating multiply imputed synthetic datasets
to guarantee confidentiality. Each chapter is dedicated to one approach, first describing the …

One approach to limiting disclosure risk in public-use microdata is to release multiply-
imputed, partially synthetic data sets. These are data on actual respondents, but with …