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Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats

Srinivasa Rao Aravilli, Sam Hamilton

Ebook
– 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
  • 1. Introduction to Data Privacy, Privacy threats and breaches
  • 2. Machine Learning Phases and privacy threats/attacks in each phase
  • 3. Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy
  • 4. Differential Privacy Algorithms, Pros and Cons
  • 5. Developing Applications with Different Privacy using open source frameworks
  • 6. Need for Federated Learning and implementing Federated Learning using open source frameworks
  • 7. Federated Learning benchmarks, startups and next opportunity
  • 8. Homomorphic Encryption and Secure Multiparty Computation
  • 9. Confidential computing - what, why and current state
  • 10. Privacy Preserving in Large Language Models
  • Tytuł: Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
  • Autor: Srinivasa Rao Aravilli, Sam Hamilton
  • Tytuł oryginału: Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
  • ISBN: 9781800564220, 9781800564220
  • Data wydania: 2024-05-24
  • Format: Ebook
  • Identyfikator pozycji: e_3wku
  • Wydawca: Packt Publishing