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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Confidential Computing – What, Why, and the Current State

Data protection is a critical consideration for enterprises that handle sensitive data, which can be personal or non-personal. There are three primary states in which data can exist within an organization: data at rest, data in motion, and data in memory. Each state has unique security and privacy concerns that require different methods of security and data protection. In this chapter, you will learn about confidential computing, including what it is, why it is required, how it helps protect data in memory attacks, and the current state of the technology.

We will cover the following main topics:

Privacy/security attacks on data in memory:
Introduction of confidential computation
Trusted execution environments (TEE) – attestation of source code and how it helps protect against insider threat attacks
Industry standards for ML in TEEs
Confidential Computing Consortium
Comparison of secure...

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Table of Contents (17) Chapters

Confidential Computing – What, Why, and the Current State

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Table of Contents (17) Chapters

Confidential Computing – What, Why, and the Current State

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