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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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Toc

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

Part 2: Use Cases of Privacy-Preserving Machine Learning and a Deep Dive into Differential Privacy

This part focuses on privacy-preserving data analysis and explores privacy-enhanced technologies, with a particular emphasis on differential privacy.

We introduce the concept of privacy-preserving data analysis and delve into techniques and methodologies that allow for the analysis of data while protecting individuals' privacy.

We highlight the risks associated with reconstruction attacks in SQL, where an adversary attempts to reconstruct sensitive information from seemingly anonymized data. We discuss various prevention methods and countermeasures that can be employed to mitigate such attacks and protect individuals' privacy.

This part also provides an overview of privacy-enhanced technologies, such as differential privacy, federated learning, secure multiparty computation (SMC), and homomorphic encryption.

We provide an introduction to differential privacy...

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