Preventing AI-driven de-anonymization requires moving beyond simple masking. While removing names helps, modern LLMs and probabilistic matching algorithms can bridge gaps between disparate datasets using quasi-identifiers like ZIP codes or birth dates.
Technically, we must adopt Differential Privacy (DP). This method injects calculated mathematical noise into datasets. By doing so, we ensure that the presence or absence of a single individual does not significantly alter the model's output. This makes record linkage mathematically improbable. Another layer involves Federated Learning (FL). Instead of pooling raw data into a central server where it can be scraped, FL allows models to train on local edge devices. Only the learned weights—not the patient records themselves—ever move across the network.
Legally, we need to update frameworks like HIPAA to address the
