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

Encryption, anonymization, and de-identification

Encryption, anonymization, and de-identification are all techniques used to protect sensitive data, but they differ in their approach and limitations.

Encryption

Encryption is the process of transforming data into a form that can only be read by authorized parties with access to a decryption key. The purpose of encryption is to ensure the confidentiality and integrity of data. Encrypted data remains readable by those who have the appropriate decryption key, but it is unintelligible to anyone who intercepts it without the key. Encryption is widely used to protect sensitive data in transit and data at rest, such as credit card numbers, passwords, and personally identifiable information.

Here’s some simple Python code to implement basic encryption

Source code: Encryption_Example.ipynb

Develop a function to encrypt the given text using a basic encryption algorithm.

def simple_encryption(text, shift):
  ...

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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

Encryption, anonymization, and de-identification

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Table of Contents (17) Chapters

Encryption, anonymization, and de-identification

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