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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Developing Applications with Differential Privacy Using Open Source Frameworks

In this chapter, we will explore open source frameworks (PyDP, PipelineDP, tmlt-analytics, PySpark, diffprivlib, PyTorch, and Opacus) used to develop machine learning, deep learning, and large-scale applications with the power of differential privacy.

We will cover the following main topics:

Open source frameworks for implementing differential privacy:
- Introduction to the PyDP framework and its key features
- Examples and demonstrations of PyDP in action
- Developing a sample banking application with PyDP to showcase differential privacy techniques
Protecting against membership inference attacks:
- Understanding membership inference attacks and their potential risks
- Techniques and strategies to safeguard against membership inference attacks when applying differential privacy
Applying differential privacy on large datasets to protect sensitive data:
- Leveraging the open source PipelineDP framework to...

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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

Developing Applications with Differential Privacy Using Open Source Frameworks

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

Developing Applications with Differential Privacy Using Open Source Frameworks

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Authors (1)

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