Practical Data Privacy: Enhancing Privacy and Security in Data, 1st Edition

.\nBetween major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help? How do I ensure that my data science projects are secure by default and private by design? How do I work with governance and infosec teams to implement internal policies appropriately?\nPractical Data Privacy: Enhancing Privacy and Security in Data 1st Edition is written by Katharine Jarmul and published by O'Reilly Media. ISBNs for Practical Data Privacy are 9781098129422, 1098129423 and the print ISBNs are 9781098129460, 1098129466. Additional ISBNs include 9781098129439.