Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats by Srinivasa Rao Aravilli
English | May 24, 2024 | ISBN: 1800564678 | 402 pages | EPUB | 10 Mb
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 FeaturesUnderstand machine learning privacy risks and employ machine learning algorithms to safeguard data against breachesDevelop and deploy privacy-preserving ML pipelines using open-source frameworksGain insights into confidential computing and its role in countering memory-based data attacksBook 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 learnStudy data privacy, threats, and attacks across different machine learning phasesExplore Uber and Apple cases for applying differential privacy and enhancing data securityDiscover IID and non-IID data sets as well as data categoriesUse open-source tools for federated learning (FL) and explore FL algorithms and benchmarksUnderstand secure multiparty computation with PSI for large dataGet 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 techniques
Table of ContentsIntroduction to Data Privacy, Privacy threats and breachesMachine Learning Phases and privacy threats/attacks in each phaseOverview of Privacy Preserving Data Analysis and Introduction to Differential PrivacyDifferential Privacy Algorithms, Pros and ConsDeveloping Applications with Different Privacy using open source frameworksNeed for Federated Learning and implementing Federated Learning using open source frameworksFederated Learning benchmarks, startups and next opportunityHomomorphic Encryption and Secure Multiparty ComputationConfidential computing - what, why and current statePrivacy Preserving in Large Language Models
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