Privacy-Preserving Machine Learning

<p><b>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 </b></p><h4>Key Features</h4><ul><li>Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches</li><li>Develop and deploy privacy-preserving ML pipelines using open-source frameworks</li><li>Gain insights into confidential computing and its role in countering memory-based data attacks</li><li>Purchase of the print or Kindle book includes a free PDF eBook</li></ul><h4>Book Description</h4>– 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 <h4>What you will learn</h4><ul><li>Study data privacy, threats, and attacks across different machine learning phases</li><li>Explore Uber and Apple cases for applying differential privacy and enhancing data security</li><li>Discover IID and non-IID data sets as well as data categories</li><li>Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks</li><li>Understand secure multiparty computation with PSI for large data</li><li>Get up to speed with confidential computation and find out how it helps data in memory attacks</li></ul><h4>Who this book is for</h4>– 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