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

Privacy breaches

What is a privacy breach?

A privacy breach, also known as a data breach, refers to an incident where unauthorized individuals or entities gain access to confidential or sensitive information without proper authorization. This breach of privacy can occur in various forms, such as hacking, theft, accidental exposure, or improper handling of data. It typically involves the unauthorized access, acquisition, disclosure, or use of personal information, which may include personally identifiable information (PII) such as names, addresses, social security numbers, financial details, and login credentials.

Privacy breaches can have serious consequences for individuals, organizations, and even society. They can lead to identity theft, financial fraud, reputational damage, loss of trust, legal implications, and emotional distress for those affected. Protecting personal data and maintaining privacy is essential to ensure the security and well-being of individuals and maintain...

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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

Privacy breaches

Authors (1)

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Table of Contents (17) Chapters

Privacy breaches

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Authors (1)

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