Bộ sách luyện thi ETS 2023 LC&RC
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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?
Categories:Computers - Security
Year:2023
Edition:1
Language:english
Pages:345
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