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Privacy-Preserving Machine Learning

You're reading from   Privacy-Preserving Machine Learning A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781800564671
Length 402 pages
Edition 1st Edition
Languages
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Author (1):
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Srinivasa Rao Aravilli Srinivasa Rao Aravilli
Author Profile Icon Srinivasa Rao Aravilli
Srinivasa Rao Aravilli
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Table of Contents (17) Chapters Close

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 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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