Privacy-Preserving ML
Privacy-Preserving Machine Learning (PPML) is a set of techniques that allow AI models to be trained and used without exposing sensitive data. It includes methods like federated learning, differential privacy, and homomorphic encryption that protect individual privacy while maintaining model utility.
As AI regulations like GDPR and CCPA tighten globally, companies face legal and ethical requirements to protect user data. Privacy-preserving ML enables companies to leverage sensitive data (healthcare, finance, personal communications) for AI development without violating privacy laws or losing customer trust.
🎓 Courses
Federated Learning: Privacy-Preserving Machine Learning
by Google Cloud Training
This course provides hands-on experience with federated learning, one of the most practical privacy-preserving ML techniques used in industry.
📖 Books
The Algorithmic Foundations of Differential Privacy
Cynthia Dwork, Aaron Roth · 2023
This updated foundational text provides the mathematical rigor needed to properly implement differential privacy in ML systems.
🛠️ Tutorials & Guides
Introduction to Homomorphic Encryption for ML
Clear explanation of homomorphic encryption concepts and their application to privacy-preserving ML.
Building Privacy-Preserving ML Systems with PySyft
Tutorial using PySyft, a popular open-source library for implementing various PPML techniques.
Learning resources last updated: April 14, 2026