Privacy-Preserving Machine Learning Techniques

This chapter explores three vital concepts in privacy-preserving machine learning: differential privacy, secure multi-party computation (MPC), and federated learning. It contrasts the efficiency of secure MPC with homomorphic encryption while emphasizing the practical advantages of decentralized computations for enhanced data privacy. The chapter also discusses future developments in frameworks for easier integration of these security protocols into popular deep learning platforms.