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 Features
- Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches
- Develop and deploy privacy-preserving ML pipelines using open-source frameworks
- Gain insights into confidential computing and its role in countering memory-based data attacks
- Purchase of the print or Kindle book includes a free PDF eBook
Book 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 learn
- Study data privacy, threats, and attacks across different machine learning phases
- Explore Uber and Apple cases for applying differential privacy and enhancing data security
- Discover IID and non-IID data sets as well as data categories
- Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks
- Understand secure multiparty computation with PSI for large data
- Get up to speed with confidential computation and find out how it helps data in memory attacks
Who 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 Contents
- Introduction to Data Privacy, Privacy threats and breaches
- Machine Learning Phases and privacy threats/attacks in each phase
- Overview of Privacy Preserving Data Analysis and Introduction to Differential Privacy
- Differential Privacy Algorithms, Pros and Cons
- Developing Applications with Different Privacy using open source frameworks
- Need for Federated Learning and implementing Federated Learning using open source frameworks
- Federated Learning benchmarks, startups and next opportunity
- Homomorphic Encryption and Secure Multiparty Computation
- Confidential computing - what, why and current state
- Privacy Preserving in Large Language Models
Srinivasa Rao Aravilli boasts 27 years of extensive experience in technology, research, and leadership roles, spearheading innovation in various domains such as Information Retrieval, Search, ML/AI, Distributed Computing, Network Analytics, Privacy, and Security. Currently working as a Senior Director of Machine Learning Engineering at Capital One, Bangalore, he has a proven track record of driving new products from conception to outstanding customer success. Prior to his tenure at Capital One, Srinivasa held prominent leadership positions at Visa, Cisco, and Hewlett Packard, where he led product groups focused on data privacy, machine learning, and Generative AI. He holds a Master's Degree in Computer Applications from Andhra University, Visakhapatnam, India.
