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

Preserving Privacy in Large Language Models

Large language models (LLMs) have emerged as a transformative technology in the field of artificial intelligence (AI), enabling advanced natural language processing (NLP) tasks and generative capabilities. These models, such as OpenAI’s GPT-3.5 and Meta’s Llama 2 have shown remarkable proficiency in generating human-like text and demonstrating a deep understanding of language patterns. In this chapter, you will learn about closed source and open source LLMs at a high level, privacy issues with these LLMs, and state-of-the-art (SOTA) research in privacy-preserving technologies for LLMs.

We will cover the following main topics:

Key concepts/terms used in LLMs
- Prompt engineering: Sentence translation using ChatGPT (closed source LLM) as well as using open source LLMs
- Comparison of open source LLMs and closed source LLMs
AI standards and terminology of attacks
- National Institute of Standards and Technology (NIST) Trustworthy...

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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

Preserving Privacy in Large Language Models

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

Preserving Privacy in Large Language Models

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