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

Comparison of HE frameworks

Let’s review a comparison of some of the most popular HE frameworks in Python.

Pyfhel

Pyfhel is a Python-based fully HE library that supports operations on encrypted integers and vectors. It is built on top of the HElib C++ library and offers a simplified interface for Python developers. Pyfhel has good performance and can handle large integers and vectors efficiently. However, it does not yet support operations on floating-point numbers.

TenSEAL

TenSEAL is a Python-based library for HE that supports both FHE and PHE. It uses the CKKS and BFV encryption schemes and offers APIs for encrypted operations on floating-point numbers and matrices. TenSEAL is designed to be easy to use and has a simpler API compared to some other HE libraries. It has a relatively high performance for encrypted operations on floating-point numbers.

PALISADE

PALISADE is a C++ library for HE that has Python bindings. It supports both FHE and PHE and offers...

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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

Comparison of HE frameworks

Pyfhel

TenSEAL

PALISADE

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

Comparison of HE frameworks

Pyfhel

TenSEAL

PALISADE

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you