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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

Clipping

As discussed earlier, unbounded queries have an infinite sensitivity value, which cannot be directly utilized to provide results with differential privacy. One approach to addressing this issue is to transform unbounded queries into bounded ones by specifying their lower and upper bounds.

In differential privacy, clipping is a technique that’s used to bind the sensitivity of a function by constraining its output within a specific range. The fundamental concept is to clip or limit the output of a function to fall into a predetermined range, such as [-c, c], where c is a positive constant. Afterward, noise is introduced to the clipped output to ensure privacy guarantees.

The clipping procedure involves two steps:

  1. Scaling the function’s output: The output of the function is scaled by dividing it by a scaling factor, denoted as s. This scaling ensures that the absolute value of the scaled output is less than or equal to the clipping threshold, c.

    Mathematically...

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