Contextual Anonymization for Secondary Use of Big Data in Biomedical Research: Proposal for an Anonymization Matrix - Rumbold, John (JMIR Med Inform, 2018)
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.