ORCID Profile
0000-0003-4184-0762
Current Organisation
City University of Hong Kong
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Wiley
Date: 22-11-2016
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11731139_101
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2008
Publisher: Elsevier BV
Date: 09-2017
Publisher: Springer Science and Business Media LLC
Date: 21-11-2018
Publisher: IEEE
Date: 13-03-2023
Publisher: Oxford University Press (OUP)
Date: 10-11-2018
DOI: 10.1093/JAMIA/OCX129
Abstract: Recent growth in the number of population health researchers accessing detailed datasets, either on their own computers or through virtual data centers, has the potential to increase privacy risks. In response, a checklist for identifying and reducing privacy risks in population health analysis outputs has been proposed for use by researchers themselves. In this study we explore the usability and reliability of such an approach by investigating whether different users identify the same privacy risks on applying the checklist to a s le of publications. The checklist was applied to a s le of 100 academic population health publications distributed among 5 readers. Cohen’s κ was used to measure interrater agreement. Of the 566 instances of statistical output types found in the 100 publications, the most frequently occurring were counts, summary statistics, plots, and model outputs. Application of the checklist identified 128 outputs (22.6%) with potential privacy concerns. Most of these were associated with the reporting of small counts. Among these identified outputs, the readers found no substantial actual privacy concerns when context was taken into account. Interrater agreement for identifying potential privacy concerns was generally good. This study has demonstrated that a checklist can be a reliable tool to assist researchers with anonymizing analysis outputs in population health research. This further suggests that such an approach may have the potential to be developed into a broadly applicable standard providing consistent confidentiality protection across multiple analyses of the same data.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2010
Publisher: Springer Science and Business Media LLC
Date: 03-1992
DOI: 10.1007/BF01189023
Location: No location found
No related grants have been discovered for Di DUAN.