– In an era of evolving privacy regulations, compliance is mandatory for every enterprise

– Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information

– This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases

– As you progress, you’ll be guided through developing anti-money laundering solutions using federated learning and differential privacy

– Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models

– You’ll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field

– Upon completion, you’ll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks

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Spis treści

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

• 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