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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
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– 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
– 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
- Title:Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
- Author:Srinivasa Rao Aravilli, Sam Hamilton
- Original title:Privacy-Preserving Machine Learning. A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
- ISBN:9781800564220, 9781800564220
- Date of issue:2024-05-24
- Format:Ebook
- Item ID: e_3wku
- Publisher: Packt Publishing
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