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Contextual Anonymization for Secondary Use of Big Data in Biomedical Research: Proposal for an Anonymization Matrix
John Rumbold;
Barbara Pierscionek
ABSTRACT
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
Please cite as:
Rumbold J, Pierscionek B
Contextual Anonymization for Secondary Use of Big Data in Biomedical Research: Proposal for an Anonymization Matrix
