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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Privacy threats/attacks in ML phases

ML projects are developed in collaboration with data engineers, ML engineers, and software engineers, and each one plays a different role in order to develop end-to-end systems to predict and provide insights.

Collaborative roles in ML projects

ML projects are collaborative efforts involving various roles such as data engineers, ML engineers, and software engineers. Each role contributes in different ways to develop end-to-end systems that can predict outcomes and provide valuable insights. Let’s explore the roles and their responsibilities in the ML project life cycle:

Data engineer: The data engineer primarily focuses on the data preparation phase. They are responsible for extracting data from one or multiple sources and ensuring its quality and suitability for the ML project. Data engineers work on tasks such as data cleaning, transformation, and feature selection to prepare the data for ML modeling.
ML engineer: ML engineers...

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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 threats/attacks in ML phases

Collaborative roles in ML projects

Authors (1)

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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 threats/attacks in ML phases

Collaborative roles in ML projects

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Authors (1)

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