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

Part 4: Homomorphic Encryption, SMC, Confidential Computing, and LLMs

This part covers homomorphic encryption and SMC. It covers, in detail, privacy preservation in large language models (LLMs).

We cover the concepts of homomorphic encryption (HE) and SMC as privacy-enhancing techniques. We highlight the significance of these cryptographic approaches in enabling secure computations on encrypted data without compromising privacy.

SMC enables multiple parties to collaboratively compute results while keeping their individual input private. Our summary emphasizes the importance of HE and SMC in scenarios where sensitive data needs to be analyzed or processed in a privacy-preserving manner. By utilizing these techniques, organizations and individuals can protect their data while still gaining valuable insights and outcomes from computations.

The second chapter in this part explores the concept of confidential computing, which aims to provide a trusted and secure environment...

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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

Part 4: Homomorphic Encryption, SMC, Confidential Computing, and LLMs

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

Part 4: Homomorphic Encryption, SMC, Confidential Computing, and LLMs

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