Privacy-Preserving ML: A Practical Guide to Balancing GDPR, Differential Privacy, and Model Utility
As machine learning models become increasingly sophisticated, the data fueling them grows more sensitive. For data engineers and ML practitioners, the challenge is no longer just about accuracy; it is about trust. How do we train powerful models on sensitive user data without violating regulation...