Protecting Privacy of Shared Epidemiologic Data without Compromising Analysis PotentialReportar como inadecuado

Protecting Privacy of Shared Epidemiologic Data without Compromising Analysis Potential - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Journal of Environmental and Public HealthVolume 2012 2012, Article ID 421989, 9 pages

Review Article

Department of Statistics, Radiation Effects Research Foundation, 5-2 Hijiyama Park, Minami-ku, Hiroshima 732-0815, Japan

Department of Epidemiology, Radiation Effects Research Foundation, Hiroshima 732-0815, Japan

Department of Medical Informatics, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Okayama 700-8558, Japan

Department of Information Technology, Radiation Effects Research Foundation, Hiroshima 732-0815, Japan

Received 17 May 2011; Accepted 2 November 2011

Academic Editor: Benny Zee

Copyright © 2012 John Cologne et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. Ensuring privacy of research subjects when epidemiologic data are shared with outside collaborators involves masking modifying the data, but overmasking can compromise utility analysis potential. Methods of statistical disclosure control for protecting privacy may be impractical for individual researchers involved in small-scale collaborations. Methods. We investigated a simple approach based on measures of disclosure risk and analytical utility that are straightforward for epidemiologic researchers to derive. The method is illustrated using data from the Japanese Atomic-bomb Survivor population. Results. Masking by modest rounding did not adequately enhance security but rounding to remove several digits of relative accuracy effectively reduced the risk of identification without substantially reducing utility. Grouping or adding random noise led to noticeable bias. Conclusions. When sharing epidemiologic data, it is recommended that masking be performed using rounding. Specific treatment should be determined separately in individual situations after consideration of the disclosure risks and analysis needs.

Autor: John Cologne, Eric J. Grant, Eiji Nakashima, Yun Chen, Sachiyo Funamoto, and Hiroaki Katayama



Documentos relacionados