Jarmul K. Practical Data Privacy: Enhancing Privacy and Security in Data

O’Reilly Media, 2023. — 342 p. — ISBN 1098129466.Between 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?Who Should Read This BookThis book is for data scientists who want to upskill themselves with a focus on data privacy and security. You could have many reasons, such as:You’d like to pursue a specialization (data privacy) that you care about, which has a long future in the industry.
You want to move into a more regulated industry like finance or healthcare, and these skills will set you up as a promising candidate in these sectors.
You work with research data, and you’d like to get faster approval from ethics board reviews and publications.
You are a data science freelancer or consultant and want to expand your customer base by ensuring that you know how to manage sensitive data.
You manage a data team and want to be able to design products and architect solutions with attention to data privacy.
You would like to use “AI for good” and think privacy is an important human right.
Your team has been told that privacy is important, but you aren’t sure of what that means or how to go about implementing it.
You work with sensitive data and want to ensure you are following best practices.
You’d like to become a privacy engineer and focus on engineering privacy into data products.
Privacy and security are neat topics, and you just enjoy learning more about them.True PDF