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

FL benchmarks

FL is a machine learning technique that allows multiple devices/clients or servers to collaboratively train a model and keep their data private. There has been an increasing need for standardized benchmarks to evaluate the performance of different FL algorithms and frameworks/platforms.

The IEEE 3652.1-2020 standard, officially titled IEEE Guide for Architectural Framework and Application of Federated Machine Learning, is a comprehensive guide that provides an architectural framework for Federated Machine Learning (FML). More details about the IEEE 3652.1-2020 standard can be found at https://ieeexplore.ieee.org/document/9382202.

FL benchmarks are datasets and evaluation metrics that are used to compare and evaluate the performance of different FL algorithms and frameworks/platforms. These benchmarks can help engineers and researchers to identify the strengths and weaknesses of different algorithms and the pros and cons of different FL frameworks.

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Table of Contents (17) Chapters

FL benchmarks

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Table of Contents (17) Chapters

FL benchmarks

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