Citation

O'Keefe CM, Rubin DB. Stat. Med. 2015; 34(23): 3081-3103.

Affiliation

Department of Statistics, Harvard University Science Center, 1 Oxford Street, Cambridge, 02138-2901, MA, U.S.A.

Copyright

(Copyright © 2015, John Wiley and Sons)

DOI

10.1002/sim.6543

PMID

26045214

Abstract

Health and medical data are increasingly being generated, collected, and stored in electronic form in healthcare facilities and administrative agencies. Such data hold a wealth of information vital to effective health policy development and evaluation, as well as to enhanced clinical care through evidence-based practice and safety and quality monitoring. These initiatives are aimed at improving individuals' health and well-being. Nevertheless, analyses of health data archives must be conducted in such a way that individuals' privacy is not compromised. One important aspect of protecting individuals' privacy is protecting the confidentiality of their data. It is the purpose of this paper to provide a review of a number of approaches to reducing disclosure risk when making data available for research, and to present a taxonomy for such approaches. Some of these methods are widely used, whereas others are still in development. It is important to have a range of methods available because there is also a range of data-use scenarios, and it is important to be able to choose between methods suited to differing scenarios. In practice, it is necessary to find a balance between allowing the use of health and medical data for research and protecting confidentiality. This balance is often presented as a trade-off between disclosure risk and data utility, because methods that reduce disclosure risk, in general, also reduce data utility. Copyright © 2015 John Wiley & Sons, Ltd.

Language: en