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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

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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