Privacy-Preserving Machine Learning
A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
Published on 24. May 2024
Book
Paperback/Softback
402 pages
978-1-80056-467-1 (ISBN)
Description
Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and algorithms to protect sensitive data from privacy breaches
Key Features
Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches
Develop and deploy privacy-preserving ML pipelines using open-source frameworks
Gain insights into confidential computing and its role in countering memory-based data attacks
Purchase of the print or Kindle book includes a free PDF eBook
Book Description- In an era of evolving privacy regulations, compliance is mandatory for every enterprise
- Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information
- This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases
- As you progress, you'll be guided through developing anti-money laundering solutions using federated learning and differential privacy
- Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models
- You'll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field
- Upon completion, you'll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks What you will learn
Study data privacy, threats, and attacks across different machine learning phases
Explore Uber and Apple cases for applying differential privacy and enhancing data security
Discover IID and non-IID data sets as well as data categories
Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks
Understand secure multiparty computation with PSI for large data
Get up to speed with confidential computation and find out how it helps data in memory attacks
Who this book is for- This comprehensive guide is for data scientists, machine learning engineers, and privacy engineers
- Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn)
- Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques
Key Features
Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches
Develop and deploy privacy-preserving ML pipelines using open-source frameworks
Gain insights into confidential computing and its role in countering memory-based data attacks
Purchase of the print or Kindle book includes a free PDF eBook
Book Description- In an era of evolving privacy regulations, compliance is mandatory for every enterprise
- Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information
- This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases
- As you progress, you'll be guided through developing anti-money laundering solutions using federated learning and differential privacy
- Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models
- You'll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field
- Upon completion, you'll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks What you will learn
Study data privacy, threats, and attacks across different machine learning phases
Explore Uber and Apple cases for applying differential privacy and enhancing data security
Discover IID and non-IID data sets as well as data categories
Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks
Understand secure multiparty computation with PSI for large data
Get up to speed with confidential computation and find out how it helps data in memory attacks
Who this book is for- This comprehensive guide is for data scientists, machine learning engineers, and privacy engineers
- Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn)
- Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 22 mm
Weight
748 gr
ISBN-13
978-1-80056-467-1 (9781800564671)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Srinivasa Rao Aravilli
Privacy-Preserving Machine Learning
A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
E-Book
09/2024
Packt Publishing
€26.99
Available for download
Persons
Srinivasa Rao Aravilli boasts 27 years of extensive experience in technology, research, and leadership roles, spearheading innovation in various domains such as Information Retrieval, Search, ML/AI, Distributed Computing, Network Analytics, Privacy, and Security. Currently working as a Senior Director of Machine Learning Engineering at Capital One, Bangalore, he has a proven track record of driving new products from conception to outstanding customer success. Prior to his tenure at Capital One, Srinivasa held prominent leadership positions at Visa, Cisco, and Hewlett Packard, where he led product groups focused on data privacy, machine learning, and Generative AI. He holds a Master's Degree in Computer Applications from Andhra University, Visakhapatnam, India.
Content
Table of Contents
Introduction to Data Privacy, Privacy threats and breaches
Machine Learning Phases and privacy threats/attacks in each phase
Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy
Differential Privacy Algorithms, Pros and Cons
Developing Applications with Different Privacy using open source frameworks
Need for Federated Learning and implementing Federated Learning using open source frameworks
Federated Learning benchmarks, startups and next opportunity
Homomorphic Encryption and Secure Multiparty Computation
Confidential computing - what, why and current state
Privacy Preserving in Large Language Models
Introduction to Data Privacy, Privacy threats and breaches
Machine Learning Phases and privacy threats/attacks in each phase
Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy
Differential Privacy Algorithms, Pros and Cons
Developing Applications with Different Privacy using open source frameworks
Need for Federated Learning and implementing Federated Learning using open source frameworks
Federated Learning benchmarks, startups and next opportunity
Homomorphic Encryption and Secure Multiparty Computation
Confidential computing - what, why and current state
Privacy Preserving in Large Language Models