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

View More author details

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

Why subscribe?

16. Other Books You May Enjoy

Secure Multiparty Computation

Secure Multiparty Computation (SMC) is a cryptographic technique that enables two or more parties to jointly compute a function on their private data without revealing their data to each other.

SMC is an important tool in privacy-preserving computation, where parties may not trust each other or a central authority and may have sensitive data that they do not want to share with other parties.

In this section, we will learn about the basic principles of SMC, its applications, and some techniques used for SMC.

Basic principles of SMC

The basic principle of SMC is that each party has private data that they want to keep secret from other parties, but they want to compute a function on the common joint data. SMC enables multiple parties to do this securely by dividing the computation into smaller, less sensitive sub-computations, which are performed locally on each party’s private data. Then, the parties communicate with each other to reveal...

The rest of the chapter is locked

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

Secure Multiparty Computation

Basic principles of SMC

Authors (1)

Personalised recommendations for you

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

Secure Multiparty Computation

Basic principles of SMC

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you