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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Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

Symbols

ℓ-diversity 89

K-anonymization dataset 90-93

original dataset 90

versus k-anonymity 96

versus t-closeness 96

adaptive federated learning (AFL) 276

Advanced Encryption Standard (AES) 285

using, for encryption example 285, 286

adversarial attacks 51

AI standards 341

NIST 341

OWASP Top 10 for LLM applications 342

Anjuna

URL 331

attestation 328

binary attestation 328

runtime attestation 328

source code attestation 328

AuthorsNN class 69

average queries 134

differential privacy algorithms, using with 136, 137

AWS Nitro Enclaves 331

Azure enclaves 331

backpropagation 66

BERT 352

black-box attack 52

BLAKE2 290

Blowfish 285

bootstrapping technique 293

Brain Tumor Segmentation (BraTS)...

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Table of Contents (17) Chapters

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Table of Contents (17) Chapters

Index

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