Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Table of Contents

1. Introduction to Data Privacy, Privacy threats and breaches
2. Machine Learning Phases and privacy threats/attacks in each phase
3. Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy
4. Differential Privacy Algorithms, Pros and Cons
5. Developing Applications with Different Privacy using open source frameworks
6. Need for Federated Learning and implementing Federated Learning using open source frameworks
7. Federated Learning benchmarks, startups and next opportunity
8. Homomorphic Encryption and Secure Multiparty Computation
9. Confidential computing - what, why and current state
10. Privacy Preserving in Large Language Models