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

Overview of ML phases

ML encompasses a variety of techniques and approaches, and it involves several distinct phases or stages in the process of developing and deploying ML models. These phases help guide engineers through the iterative and cyclical nature of ML projects, allowing them to build effective and accurate models.

The ML process typically consists of several key phases, each serving a specific purpose and contributing to the overall success of the project. These phases are not always strictly linear, and iterations may occur between them to refine and improve the models. The specific steps and terminology used may vary depending on the ML methodology employed, but the core phases remain consistent.

The ML phases provide a systematic framework for developing and deploying ML models, guiding practitioners through the complexities and challenges inherent in building effective solutions. By following these phases, practitioners can maximize their chances of success and...

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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

Overview of ML phases

Authors (1)

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Table of Contents (17) Chapters

Overview of ML phases

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