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Synopsis
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?
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About the Author
Katharine Jarmul is a privacy activist, machine learning engineer, and principal data scientist at Thoughtworks Germany. She is also a passionate and internationally recognized data scientist, programmer, and lecturer. Previously, Katharine held numerous roles at large companies and startups in the US and Germany, implementing data processing and machine learning systems with a focus on reliability, testability, privacy and security. She is an O'Reilly author and a frequent keynote speaker at international software and AI conferences.
For the past five years, Katharine has focused on answering the question: How do we perform privacy-aware data science and machine learning? To answer this question, she's worked on the legal and technical aspects of regulations like GDPR, as well as helped build an encrypted learning platform based on multi-party computation.
"About this title" may belong to another edition of this title.
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Paperback. Condition: New. Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure for data scientists to ensure data privacy. Unfortunately, integrating privacy into your data science workflow is still complicated. This essential guide will give you solid advice and best practices on breakthrough privacy-enhancing technologies such as encrypted learning and differential privacy--as well as a look at emerging technologies and techniques in the field.Practical Data Privacy answers important questions such as:What do privacy regulations like GDPR and CCPA mean for my project?What does "anonymized data" really mean?Should I anonymize the data? If so, how?Which privacy techniques fit my project and how do I incorporate them?What are the differences and similarities between privacy-preserving technologies and methods?How do I utilize an open-source library for a privacy-enhancing technique?How do I ensure that my projects are secure by default and private by design?How do I create a plan for internal policies or a specific data project that incorporates privacy and security from the start? Seller Inventory # LU-9781098129460
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