Differential Privacy for Machine Learning

Teaching models to learn without giving away secrets

A ground-up journey through privacy in machine learning — from understanding why trained models leak information, to threat models and privacy attacks, to the mathematical guarantees of differential privacy and how to build systems that are both useful and safe.