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

Open source frameworks to implement differential privacy

There are several open source frameworks available to implement differential privacy. We will go through the PyDP framework in detail in this section.

Introduction to the PyDP framework and its key features

Google has released an open source framework called differential privacy that facilitates the implementation of differential privacy. This framework offers support for both ε- and (ε, δ)-differentially private statistics. It includes various features such as the ability to introduce noise using Laplace and Gaussian mechanisms. Additionally, the framework provides support for aggregated differential privacy algorithms including sum, count, mean, variance, and standard deviation. The libraries within this framework are implemented in the C++, Java, and Go languages, and it also offers a command-line interface (CLI) to execute differential privacy SQL queries. For further information, you can visit the...

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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

Open source frameworks to implement differential privacy

Introduction to the PyDP framework and its key features

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

Open source frameworks to implement differential privacy

Introduction to the PyDP framework and its key features

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Authors (1)

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