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

The need for privacy-preserving ML

Privacy-preserving ML has emerged as a response to the growing concerns about data privacy in AI and ML and the need to protect sensitive information while leveraging the power of ML algorithms. You will learn about ML and privacy-preserving ML techniques in the next chapter.

The following are some of the reasons why privacy-preserving ML is necessary:

Protection of sensitive data: Privacy-preserving ML techniques enable organizations to utilize sensitive data without exposing it to potential breaches or unauthorized access. By implementing privacy safeguards, organizations can protect sensitive information such as PII, medical records, financial data, or proprietary business data. You will learn more in Chapters 5 to 8 on how to protect sensitive data using different privacy-preserving technologies.
Compliance with privacy regulations: Many jurisdictions have enacted strict privacy regulations and data protection laws, such as 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

The need for privacy-preserving ML

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Table of Contents (17) Chapters

The need for privacy-preserving ML

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