Individual privacy versus public good: protecting confidentiality in health research

被引:36
作者
O'Keefe, Christine M. [1 ]
Rubin, Donald B. [2 ]
机构
[1] CSIRO, Digital Prod Flagship, Lyneham, ACT 2602, Australia
[2] Harvard Univ, Ctr Sci, Dept Stat, Cambridge, MA 02138 USA
关键词
confidentiality; privacy; biostatistics; medical research; health care research; STATISTICAL DISCLOSURE LIMITATION; RISK-UTILITY PARADIGMS; MULTIPLE IMPUTATION; MISSING DATA; ACCESS; MICRODATA; SENSITIVITY; FRAMEWORK; QUALITY; PERSPECTIVES;
D O I
10.1002/sim.6543
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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 (c) 2015John Wiley & Sons, Ltd.
引用
收藏
页码:3081 / 3103
页数:23
相关论文
共 110 条
[1]  
Abowd J, 2006, TECHNICAL REPORT
[2]  
ADAM NR, 1989, COMPUT SURV, V21, P515, DOI 10.1145/76894.76895
[3]   Multiple imputation: an alternative to top coding for statistical disclosure control [J].
An, Di ;
Little, Roderick J. A. .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 2007, 170 :923-940
[4]   A multiple imputation approach to disclosure limitation for high-age individuals in longitudinal studies [J].
An, Di ;
Little, Roderick J. A. ;
McNally, James W. .
STATISTICS IN MEDICINE, 2010, 29 (17) :1769-1778
[5]  
[Anonymous], 1999, AUST NZ J PUBL HEAL, DOI DOI 10.1111/j.1467-842X.1999.tb01297.x
[6]  
[Anonymous], 2010, AM J BIOETHICS, DOI DOI 10.1080/15265161.2010.494215
[7]  
[Anonymous], 19 U OXF
[8]  
[Anonymous], P 58 C INT STAT I IS, DOI DOI 10.1136/amiajnl-2012-000936
[9]  
[Anonymous], ADV DIGITAL GOVT TEC
[10]  
Australian Bureau of Statistics, CURF MICR