GAIN HANDS-ON EXPERIENCE IN DATA PRIVACY AND PRIVACY-PRESERVING MACHINE LEARNING WITH OPEN-SOURCE ML FRAMEWORKS, WHILE EXPLORING TECHNIQUES AND ALGORITHMS TO PROTECT SENSITIVE DATA FROM PRIVACY BREACHES KEY FEATURES * Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches * Develop and deploy privacy-preserving ML pipelines using open-source frameworks * Gain insights into confidential computing and its role in countering memory-based data attacks * Purchase of the print or Kindle book includes a free PDF eBook BOOK DESCRIPTION – 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 WHAT YOU WILL LEARN * Study data privacy, threats, and attacks across different machine learning phases * Explore Uber and Apple cases for applying differential privacy and enhancing data security * Discover IID and non-IID data sets as well as data categories * Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks * Understand secure multiparty computation with PSI for large data * Get up to speed with confidential computation and find out how it helps data in memory attacks WHO THIS BOOK IS FOR – This comprehensive guide is for data scientists, machine learning engineers, and privacy engineers – Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn) – Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques