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Privacy-Preserving Machine Learning: A use-case-driven approach to building and pr...
Description material
epub | 17.2 MB | English | Isbn:9781800564220 | Author: Srinivasa Rao Aravilli, Sam Hamilton (Foreword by) | Year: 2024
About ebook: Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
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
Key Features
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
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
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