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

Exploring the mathematics behind HE

The mathematics behind HE is based on two main concepts: encryption and homomorphism.

Encryption

Encryption is the process of transforming plaintext into ciphertext using an encryption algorithm and a secret key. The ciphertext can then be transmitted over a network or stored in a database without fear of unauthorized access. To decrypt the ciphertext and obtain the plaintext, the recipient must possess the secret key that was used to encrypt the data.

Homomorphism

Homomorphism is a mathematical property that allows an operation to be performed on ciphertexts, generating a new ciphertext that is the result of the operation on the plaintexts. This means that if we have two plaintexts x and y, and their respective ciphertexts C(x) and C(y), we can perform an operation on C(x) and C(y) to obtain a new ciphertext C(x+y), which can be decrypted to obtain the result of the operation on x and y.

The most commonly used homomorphic operations...

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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

Exploring the mathematics behind HE

Encryption

Homomorphism

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

Exploring the mathematics behind HE

Encryption

Homomorphism

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you