Privacy-Preserving Machine Learning: Principles, Practice and Challenges

This comprehensive study examines methods for developing machine learning systems that protect individual privacy while maintaining high performance. The research analyzes various privacy-preserving techniques including differential privacy, federated learning, and secure multi-party computation.

The authors provide practical guidelines for implementing privacy-preserving ML systems and evaluate the trade-offs between privacy guarantees and model utility.

The paper also addresses emerging challenges in privacy-preserving ML, including new attack vectors and regulatory compliance requirements.