You're reading from Privacy-Preserving Machine Learning A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Product type Paperback

Published in May 2024

Publisher Packt

ISBN-13 9781800564671

Length 402 pages

Edition 1st Edition

Languages

Python

Tools

Federated Learning

Concepts

Machine Learning

Author (1):

Srinivasa Rao Aravilli

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Table of Contents (17) Chapters

Preface

1. Part 1: Introduction to Data Privacy and Machine Learning FREE CHAPTER

2. Chapter 1: Introduction to Data Privacy, Privacy Breaches, and Threat Modeling

3. Chapter 2: Machine Learning Phases and Privacy Threats/Attacks in Each Phase

4. Part 2: Use Cases of Privacy-Preserving Machine Learning and a Deep Dive into Differential Privacy

5. Chapter 3: Overview of Privacy-Preserving Data Analysis and an Introduction to Differential Privacy

6. Chapter 4: Overview of Differential Privacy Algorithms and Applications of Differential Privacy

7. Chapter 5: Developing Applications with Differential Privacy Using Open Source Frameworks

8. Part 3: Hands-On Federated Learning

9. Chapter 6: Federated Learning and Implementing FL Using Open Source Frameworks

10. Chapter 7: Federated Learning Benchmarks, Start-Ups, and the Next Opportunity

11. Part 4: Homomorphic Encryption, SMC, Confidential Computing, and LLMs

12. Chapter 8: Homomorphic Encryption and Secure Multiparty Computation

13. Chapter 9: Confidential Computing – What, Why, and the Current State

14. Chapter 10: Preserving Privacy in Large Language Models

15. Index

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16. Other Books You May Enjoy

Introduction to Data Privacy, Privacy Breaches, and Threat Modeling

Privacy-preserving machine learning (ML) is becoming increasingly important in today’s digital age, where the use of personal data is ubiquitous in various industries, including healthcare, finance, and marketing. While ML can bring many benefits, such as improved accuracy and efficiency, it also raises significant concerns about privacy and security. Many individuals are increasingly concerned about the risks associated with the use of their personal data, including unauthorized access, misuse, and abuse. Furthermore, there are regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) that require organizations to comply with strict privacy guidelines while processing personal data.

This book provides a comprehensive understanding of the techniques and tools available to protect individuals’ privacy while enabling effective ML. This book will...

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You're reading from Privacy-Preserving Machine Learning A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Table of Contents (17) Chapters

Introduction to Data Privacy, Privacy Breaches, and Threat Modeling

Authors (1)

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You're reading from Privacy-Preserving Machine Learning A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Table of Contents (17) Chapters

Introduction to Data Privacy, Privacy Breaches, and Threat Modeling

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Authors (1)

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