Evaluation of the Anonymity and Utility of De-Identified Clinical Data Based on Japanese Anonymization Criteria

Eizen Kimura, Satoshi Hasegawa, Koji Chida, Shoko Gamo, Satoshi Irino, Haku Ishida, Yukio Kurihara

Abstract: We analyze the deterioration of clinical data quality due to anonymization. The result shows that data quality remained high with micro-aggregation and also verify the availability of noise addition to prevent illegal re-identification by matching another personal data.