User-Aware Deep Learning Anonymization for Enhanced Privacy in Medical Record Sharing
As medical records are shared for research and treatment, the same anonymization techniques are used for all, but the risks are exacerbated by the fact that the records are being shared with others. This paper proposes a user-aware deep learning anonymization framework to adjust privacy strength according to the recipient attributes (such as the role, purpose, trust level). It combines conditional generative adversarial networks with differential privacy, with the privacy budget allocated to a user, ε, being tighter for low-trust requesters, and looser for clinicians, where ε ∈ {0.1, 1.0, 5.0}. The proposed approach performs well downstream in terms of the F1 score (0.89) of high-trust users, and exhibits membership inference AUC of 0.52 just above random guessing on the MIMIC-IV data. The accuracy of attribute inference for low-trust users is also low (still less than 0.55) for sensitive diagnoses, leading to improved performance over fixed-DP GAN, AnomiGAN, and PRIMIS. A user encoder is able to decrease mutual information between embeddings and raw patient data from 0.42 to 0.07 during training. In conclusion, the results show that user-adaptive anonymization maintains clinically meaningful utility for legitimate use while ensuring strong protection against re-identification attempts, thereby filling a crucial gap in the field of privacy-preserving medical data sharing.