Privacy-Preserving Machine Learning

<p><strong>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</strong></p><p><strong>Key Features:</strong></p><p>- Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches</p><p>- Develop and deploy privacy-preserving ML pipelines using open-source frameworks</p><p>- Gain insights into confidential computing and its role in countering memory-based data attacks</p><p>- Purchase of the print or Kindle book includes a free PDF eBook</p><p><strong>Book Description:</strong></p><p>- In an era of evolving privacy regulations compliance is mandatory for every enterprise</p><p>- Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information</p><p>- 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</p><p>- As you progress you'll be guided through developing anti-money laundering solutions using federated learning and differential privacy</p><p>- Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models</p><p>- You'll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks as well as frontier research in the field</p><p>- 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</p><p><strong>What You Will Learn:</strong></p><p>- Study data privacy threats and attacks across different machine learning phases</p><p>- Explore Uber and Apple cases for applying differential privacy and enhancing data security</p><p>- Discover IID and non-IID data sets as well as data categories</p><p>- Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks</p><p>- Understand secure multiparty computation with PSI for large data</p><p>- Get up to speed with confidential computation and find out how it helps data in memory attacks</p><p><strong>Who this book is for:</strong></p><p>- This comprehensive guide is for data scientists machine learning engineers and privacy engineers</p><p>- Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow PyTorch or scikit-learn)</p><p>- Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques</p><p><strong>Table of Contents</strong></p><p>- Introduction to Data Privacy Privacy threats and breaches</p><p>- Machine Learning Phases and privacy threats/attacks in each phase</p><p>- Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy</p><p>- Differential Privacy Algorithms Pros and Cons</p><p>- Developing Applications with Different Privacy using open source frameworks</p><p>- Need for Federated Learning and implementing Federated Learning using open source frameworks</p><p>- Federated Learning benchmarks startups and next opportunity</p><p>- Homomorphic Encryption and Secure Multiparty Computation</p><p>- Confidential computing - what why and current state</p><p>- Privacy Preserving in Large Language Models</p>