— 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
