Privacy-Preserving Machine Learning (MEAP)
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English | 2022 | ISBN: 978-1617298042 | 323 pages | True PDF | 13.24 MB
Complex privacy-enhancing technologies are demystified through real-world use cases for facial recognition, cloud data storage, and more.
Privacy-Preserving Machine Learning is a practical guide to keeping ML data anonymous and secure. You’ll learn the core principles behind different privacy preservation technologies, and how to put theory into practice for your own machine learning.
Complex privacy-enhancing technologies are demystified through real-world use cases for facial recognition, cloud data storage, and more. Alongside skills for technical implementation, you’ll learn about current and future machine learning privacy challenges and how to adapt technologies to your specific needs. By the time you’re done, you’ll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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