Exploring the tradeoff between data privacy and utility with a clinical data analysis use case
Eunyoung Im,
Hyeoneui Kim,
Hyungbok Lee
et al.
Abstract:Background
Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset’s utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a data… Show more
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