Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Dec 6, 2016
Date Accepted: Jun 21, 2018
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Contextual Anonymization for Secondary Use of Big Data in Biomedical Research: Proposal for an Anonymization Matrix
Background:
The current law on anonymization sets the same standard across all situations, which poses a problem for biomedical research.
Objective:
We propose a matrix for setting different standards, which is responsive to context and public expectations.
Methods:
The law and ethics applicable to anonymization were reviewed in a scoping study. Social science on public attitudes and research on technical methods of anonymization were applied to formulate a matrix.
Results:
The matrix adjusts anonymization standards according to the sensitivity of the data and the safety of the place, people, and projects involved.
Conclusions:
The matrix offers a tool with context-specific standards for anonymization in data research.
Citation
