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Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning

By : Srinivasa Rao Aravilli
5 (8)
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Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning

5 (8)
By: Srinivasa Rao Aravilli

Overview of this book

– 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
Table of Contents (17 chapters)
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Free Chapter
1
Part 1: Introduction to Data Privacy and Machine Learning
4
Part 2: Use Cases of Privacy-Preserving Machine Learning and a Deep Dive into Differential Privacy
8
Part 3: Hands-On Federated Learning
11
Part 4: Homomorphic Encryption, SMC, Confidential Computing, and LLMs

Introduction to Data Privacy, Privacy Breaches, and Threat Modeling

Privacy-preserving machine learning (ML) is becoming increasingly important in today’s digital age, where the use of personal data is ubiquitous in various industries, including healthcare, finance, and marketing. While ML can bring many benefits, such as improved accuracy and efficiency, it also raises significant concerns about privacy and security. Many individuals are increasingly concerned about the risks associated with the use of their personal data, including unauthorized access, misuse, and abuse. Furthermore, there are regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) that require organizations to comply with strict privacy guidelines while processing personal data.

This book provides a comprehensive understanding of the techniques and tools available to protect individuals’ privacy while enabling effective ML. This book will...

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