The cost of quality: Implementing generalization and suppression for anonymizing biomedical data with minimal information loss.

•We study a frequently recommended transformation model for health data anonymization.•We prove that common assumptions about privacy models do not hold in this context.•As a consequence, existing approaches provide only sub-optimal data quality.•We show that a simple alternative method is inefficient in terms of execution times.•We propose a novel approach to overcome these limitations.