Privacy-Preserving Machine Learning by Srinivasa Rao Aravilli (.ePUB)
File Size: 10 MB
Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats by Srinivasa Rao Aravilli (Author), Sam Hamilton (Foreword)
Requirements: .ePUB reader, 10 MB
Overview: 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
Genre: Non-Fiction > Tech & Devices
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