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Privacy-Preserving Machine Learning - by Srinivasa Rao Aravilli (Paperback)
About this item
Highlights
- 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 breachesKey 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 eBookBook 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 attacksWhat 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 attacksWho 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 techniquesTable 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
- Author(s): Srinivasa Rao Aravilli
- 402 Pages
- Computers + Internet, Security
Description
About the Book
This book helps software engineers, data scientists, ML and AI engineers, and research and development teams to learn and implement privacy-preserving machine learning as well as protect companies against privacy breaches.
Book Synopsis
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
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