  Typical personal medical data contains sensitive information about
individuals. Storing or sharing the personal medical data is thus often risky.
For example, a short DNA sequence can provide information that can not only
identify an individual, but also his or her relatives. Nonetheless, most
countries and researchers agree on the necessity of collecting personal medical
data. This stems from the fact that medical data, including genomic data, are
an indispensable resource for further research and development regarding
disease prevention and treatment. To prevent personal medical data from being
misused, techniques to reliably preserve sensitive information should be
developed for real world application. In this paper, we propose a framework
called anonymized generative adversarial networks (AnomiGAN), to improve the
maintenance of privacy of personal medical data, while also maintaining high
prediction performance. We compared our method to state-of-the-art techniques
and observed that our method preserves the same level of privacy as
differential privacy (DP), but had better prediction results. We also observed
that there is a trade-off between privacy and performance results depending on
the degree of preservation of the original data. Here, we provide a
mathematical overview of our proposed model and demonstrate its validation
using UCI machine learning repository datasets in order to highlight its
utility in practice. Experimentally, our approach delivers a better performance
compared to that of the DP approach.
