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

Federated learning

FL has emerged as a solution to address the challenges of traditional centralized Machine Learning (ML) approaches in scenarios where data privacy and data locality are of paramount importance.

The key reasons that we need FL are as follows:

Preserving data privacy: In many situations, data is sensitive and cannot be shared due to legal, ethical, or privacy concerns. FL enables you to train models directly on distributed data sources without sharing the raw data, ensuring privacy protection. By keeping data local and performing model updates locally, FL minimizes the risk of exposing sensitive information.
Data localization and regulatory compliance: FL allows organizations to comply with data localization requirements and regulations. Instead of transferring data to a central server, data remains within the jurisdiction where it is generated or collected, addressing concerns related to cross-border data transfers.
Scalability and efficiency: Centralized...

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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

Federated learning

Authors (1)

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Table of Contents (17) Chapters

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