Privacy-Preserving Machine Learning: A use-case-driven approach to building and pr...

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