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 ...
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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 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?
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