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

Overview of Differential Privacy Algorithms and Applications of Differential Privacy

The concept of differential privacy holds great significance in the realm of data privacy and its importance continues to grow as more and more data is collected and analyzed. Differential privacy algorithms offer a means to safeguard individual privacy while still allowing for valuable insights to be derived from this data.

In this chapter, we will gain an overview of differential privacy algorithms, along with a comprehension of crucial concepts such as sensitivity and clipping in the context of differential privacy. Additionally, we will explore how aggregates are generated through the use of differential privacy, including in real-world applications.

The following main topics will be covered in this chapter:

Differential privacy algorithms:
- The Laplace algorithm for differential privacy
- The Gaussian algorithm for differential privacy
- Generating aggregates using differential privacy

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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

Overview of Differential Privacy Algorithms and Applications of Differential Privacy

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Table of Contents (17) Chapters

Overview of Differential Privacy Algorithms and Applications of Differential Privacy

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