Practical Data Privacy: Enhancing Privacy and Security in Data 1st Edition 273958
Код товару: 273958Паперова книга
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ISBN978-1098129460
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Бренд
-
Автор
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Рік2023
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МоваАнглійська
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ІлюстраціїЧорно-білі
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?
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.
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Характеристики
- Бренд
- Автор
- КатегоріяПрограмування
- Рік2023
- Сторінок344
- Формат165х235 мм
- ОбкладинкаМ'яка
- Тип паперуОфсетний
- МоваАнглійська
- ІлюстраціїЧорно-білі
Від видавця
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?
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.
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