This book aims to help software engineers, data scientists, ML and AI engineers, research and development teams to learn and implement privacy preserved machine learning and protect ( 2 to 4%) company revenues from privacy breaches Privacy regulations are evolving each year and compliance to privacy regulations are mandatory for every enterprise. At the same time Machine Learning Engineers need to analyze large amounts of data to predict various insights and be compliant with privacy regulations to protect the sensitive data. This is quite challenging because of large volumes of data, and lack of in-depth expertise in Privacy Preserved Machine Learning. This book helps the reader to know about data privacy, privacy threats in machine learning, real world use cases of privacy preserved machine learning and open source frameworks to implement the same. First of its kinds of book to learn how to develop and deploy anti-money laundering use case in privacy preserved manner by implementing Federated Learning and Differential privacy. Learn about data in memory attacks and how to protect data and ML models from various privacy and security threats. At the end learn about need for confidential computation and various benchmarks of privacy preserved machine learnings and state of the art research in this space. This book is for Data Scientists, Machine Learning Engineers, Privacy Engineers who have working knowledge in mathematics, have basic knowledge in any one of the ML Frameworks (TensorFlow, PyTorch, Scikit Learning). This book helps to develop ML pipelines in a privacy preserved manner and comply with data privacy regulations (CCPA, GDPR) across the world.

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

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