Practical Data Privacy: Enhancing Privacy and Security in Data

Amidst the backdrop of stringent privacy regulations like the GDPR and CCPA, coupled with the looming threat of costly and high-profile data breaches, the imperative to safeguard data privacy has never been more pronounced. Regrettably, the process of incorporating privacy measures into data systems remains a complex undertaking. This indispensable handbook aims to provide you with a foundational comprehension of contemporary privacy components, including differential privacy, federated learning, and encrypted computation. Drawing from invaluable experience, this book imparts sound guidance and best practices for seamlessly integrating groundbreaking privacy-enhancing technologies into operational systems.

"Practical Data Privacy" addresses pivotal inquiries such as:

What implications do privacy regulations like GDPR and CCPA carry for my data workflows and data science applications? What is the true essence of "anonymized data," and how can I effectively anonymize data? How does federated learning and analysis function? While homomorphic encryption appears promising, is it presently fit for practical use? How can I assess and select the most suitable privacy-preserving technologies and methodologies? Are there open-source libraries available to assist with this? How can I ensure that my data science initiatives are inherently secure and inherently private? What strategies can I employ to collaborate with governance and information security teams in the proper implementation of internal policies?