Two methods to de-identify large patient datasets greatly reduced risk of re-identification

Two de-identification methods, k-anonymization and adding a ‘fuzzy factor,’ significantly reduced the risk of re-identification of patients in a dataset of 5 million patient records from a large cervical cancer screening program in Norway.

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