Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781800564671
Length 402 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Srinivasa Rao Aravilli Srinivasa Rao Aravilli
Author Profile Icon Srinivasa Rao Aravilli
Srinivasa Rao Aravilli
Arrow right icon
View More author details
Toc

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

Privacy-preserving techniques

It’s important to note that data privacy is a fundamental right, and individuals have the right to control how their personal information is collected, used, and shared. Moreover, as discussed above, a lack of data privacy protection can lead to serious consequences such as identity theft, financial fraud, and discrimination.

In the context of data analysis, an adversary refers to an entity or party that actively seeks to gain unauthorized access to, or exploit, sensitive data, disrupt the data analysis process, or manipulate the results for their own benefit or malicious intent. Adversaries in data analysis can include individuals, organizations, or automated systems that aim to compromise the integrity, confidentiality, or availability of the data or the analysis process itself.

Organizations can implement various privacy-preserving techniques that allow for the analysis of data while still protecting the privacy of individuals. We’...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at £16.99/month. Cancel anytime