Multiple imputation is a straightforward method for handling missing data in a principled
fashion. This paper presents an overview of multiple imputation, including important …

Demand for access to data, especially data collected using public funds, is ever growing. At
the same time, concerns about the disclosure of the identities of and sensitive information …

Access to electronic health record (EHR) data has motivated computational advances in
medical research. However, various concerns, particularly over privacy, can limit access to …

Differential Privacy: A Primer for a Non-Technical Audience Page 1 Differential Privacy: A
Primer for a Non-Technical Audience Alexandra Wood, Micah Altman, Aaron Bembenek, Mark …

The federal statistical system is experiencing competing pressures for change. On the one
hand, for confidentiality reasons, much socially valuable data currently held by federal …

In many contexts, confidentiality constraints severely restrict access to unique and valuable
microdata. Synthetic data which mimic the original observed data and preserve the …

This article evaluates the use of the multiple imputation framework to protect the
confidentiality of respondents' answers in sample surveys. The basic proposal is to simulate …

To limit disclosure risks, one approach is to release partially synthetic, public use microdata
sets. These comprise the units originally surveyed, but some collected values, for example …

Synthetic data generation has emerged as a promising method to protect patient privacy
while sharing individual-level health data. Intuitively, sharing synthetic data should reduce …

The aim of this book is to give the reader a detailed introduction to the different approaches
to generating multiply imputed synthetic datasets. It describes all approaches that have been …