Kimura Eizen, Hasegawa Satoshi, Chida Koji, Gamo Shoko, Irino Satoshi, Ishida Haku, Kurihara Yukio
Department of Medical Informatics, Ehime University Graduate School of Medicine, Touon, Ehime, Japan.
NTT Secure Platform Laboratories, Musashino, Tokyo, Japan.
Stud Health Technol Inform. 2017;245:1303.
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.
我们分析了因匿名化导致的临床数据质量恶化情况。结果表明，通过微聚集数据质量仍保持较高水平，并且还验证了添加噪声以防止通过匹配其他个人数据进行非法重新识别的可行性。
