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

Обкладинка - м"яка

Рік видання - 2024

Кількість сторінок - 402

Папір - білий, офсет

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

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

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