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

View More author details

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

Why subscribe?

16. Other Books You May Enjoy

Open source frameworks to implement FL

There are a few open source frameworks to implement FL at scale. The following are some of the most popular.

PySyft (https://github.com/OpenMined/PySyft), developed by OpenMined, is an open source stack that offers secure and private data science capabilities in Python. It introduces a separation between private data and model training, enabling functionalities such as FL, differential privacy, and encrypted computation. Initially, PySyft utilized the Opacus framework to support differential privacy, as discussed in the Differential privacy chapter. However, the latest version of PySyft incorporates its own differential privacy component to provide enhanced functionality and efficiency in preserving privacy while performing data analysis tasks.

TensorFlow Federated

TensorFlow Federated (TFF) is a library developed by Google that facilitates the training of shared ML models across multiple clients using their local data (https://www.tensorflow...

The rest of the chapter is locked

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

Open source frameworks to implement FL

TensorFlow Federated

Authors (1)

Personalised recommendations for you

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

Open source frameworks to implement FL

TensorFlow Federated

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you