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

Industry standards for ML in TEEs

Architectures are defined by various standard bodies in order to train ML models with encrypted data and deploy them in third-party TEEs for execution.

IEEE 2830-2021 is one of the standards defined by IEEE as the Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning standard (https://ieeexplore.ieee.org/document/9586768).

Functional components, workflows, security requirements, technical requirements, and protocols are specified in this standard for executing ML applications in TEEs. The high-level protocol steps defined in this standard are as follows:

Data providers download and deploy tools from the computation platform.
Data providers carry out data preparation, which includes data encryption and authorization.
Encrypted data is uploaded to the computation platform by the data providers.
The task initiator starts computation tasks on the platform, which include the model to...

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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

Industry standards for ML in TEEs

Authors (1)

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Table of Contents (17) Chapters

Industry standards for ML in TEEs

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