Balancing Data Transparency and Privacy Protection in Epidemiological Visualizations: A Targeted Narrative Review of Privacy-Preserving Data Visualizations

Abstract

Epidemiology relies on data visualization to identify disease patterns, causes, and effects. These visualizations often contain sensitive information that risks re-identification if not properly anonymized, while excessive anonymization may lead to information loss, distort findings, and reduce usability. This review summarizes strategies for balancing transparency with privacy preservation in epidemiological visualizations. Through targeted database searches, we identified common anonymization strategies, adaptable for various visualizations. Each visualization type requires a different approach, and often a combination of methods to yield the strongest privacy protection. Privacy-preserving visualization remains underdeveloped, and further empirical validation and user studies are needed.