Data Privacy in der Praxis

Proven practices for improving privacy from technical, organizational, and legal perspectives

How can I ensure that the data in my project is private, anonymized, and secure? Tailored to the European market, this guide also thoroughly addresses the GDPR. It includes topics such as ChatGPT and Deep Fakes. Katharine Jarmul is a renowned privacy specialist, working for Thoughtworks and co-founder of PyLadies. The pressure to ensure data protection has never been greater than it is today. However, integrating privacy into data systems remains complex. This guide provides data scientists and data engineers with a fundamental understanding of modern privacy components such as differential privacy, federated learning, and homomorphic encryption. It offers well-founded advice and best practices for employing groundbreaking technologies to enhance data protection in production systems. Privacy expert Katharine Jarmul illustrates in this book the impacts of the GDPR and the California Consumer Privacy Act (CCPA) on your data science projects. She explains how data is anonymized, which privacy techniques are suitable for your projects, and how to integrate them. You will also learn how to create a plan for your data project that considers privacy and security from the outset. Katharine Jarmul combines a solid technical perspective with easily understandable overviews of the latest technological approaches and architectures.