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

Privacy-preserving technologies for LLMs

Differential privacy is one of the privacy-preserving technologies that can be used for LLMs as well.

Text attacks on ML models and LLMs

TextAttack stands as a Python framework designed for conducting adversarial attacks, adversarial training, and data augmentation within the field of NLP. This versatile tool streamlines the process of exploring NLP model robustness, offering a seamless, rapid, and user-friendly experience. Furthermore, it proves invaluable for NLP model training, adversarial training, and data augmentation purposes. TextAttack offers various components tailored for typical NLP tasks, including sentence encoding, grammar checking, and word replacement, which can also be utilized independently.

Instructions on how to install the TextAttack package can be found at this GitHub URL: https://github.com/QData/TextAttack.

Install TextAttack framework using pip install in the following way:

!pip install textattack

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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

Privacy-preserving technologies for LLMs

Text attacks on ML models 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

Privacy-preserving technologies for LLMs

Text attacks on ML models and LLMs

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Authors (1)

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