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

The LINDDUN framework

LINDDUN is a privacy threat modeling methodology that supports analysts in systematically eliciting and mitigating privacy threats in software architectures. This framework was developed by privacy experts at KU Leuven.

The LINDDUN framework consists of three main steps:

Model the system.
Elicit threats.
Mitigate threats.

Figure 1.2 – LINDDUN framework steps

Step 1 – modeling the system

In this step, it is crucial to gain a comprehensive understanding of the system or product being developed, including detailed knowledge of data flows. This entails comprehending how data is collected, processed, utilized, retained, and shared, as well as identifying the system’s users and their access methods. Additionally, it is essential to understand how the system interacts with other systems. To facilitate the analysis of privacy threats, LINDDUN employs DFDs as a means of capturing system or product...

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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

The LINDDUN framework

Step 1 – modeling the system

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

The LINDDUN framework

Step 1 – modeling the system

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Authors (1)

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