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

Applying differential privacy to large datasets

In the previous examples, we focused on calculating differentially private aggregates (such as count, sum, and average) on smaller datasets, involving a single terminal or a limited number of terminals. However, in this section, we will explore how to generate differentially private aggregates on large datasets, including millions or even billions of records. Specifically, we will consider a use case involving a dataset of approximately 5 million credit card transactions across 1,000 point-of-sale terminals and 5,000 customers.

Use case – generating differentially private aggregates on a large dataset

Let’s generate the dataset comprising credit card transactions recorded on a daily basis across 1,000 POS terminals. These transactions involve a total of 5,000 customers, resulting in an extensive collection of approximately 5 million records.

To calculate differentially private aggregates on such a large dataset...

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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

Applying differential privacy to large datasets

Use case – generating differentially private aggregates on a large dataset

Authors (1)

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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

Applying differential privacy to large datasets

Use case – generating differentially private aggregates on a large dataset

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Authors (1)

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