Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats - Paperback

by Srinivasa Rao Aravilli (Author), Sam Hamilton (Foreword by)

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

Table of Contents

- Introduction to Data Privacy, Privacy threats and breaches

- Machine Learning Phases and privacy threats/attacks in each phase

- Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy

- Differential Privacy Algorithms, Pros and Cons

- Developing Applications with Different Privacy using open source frameworks

- Need for Federated Learning and implementing Federated Learning using open source frameworks

- Federated Learning benchmarks, startups and next opportunity

- Homomorphic Encryption and Secure Multiparty Computation

- Confidential computing - what, why and current state

- Privacy Preserving in Large Language Models