Privacy-Preserving Machine Learning - MEAP Version 8
J. Morris Chang, Di Zhuang, Gamage Dumindu Samaraweera
All chapters available.
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.
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.
Tahun:
2022
Edisi:
MEAP Edition
Penerbit:
Manning Publications
Bahasa:
english
ISBN 10:
1617298042
ISBN 13:
9781617298042
Fail:
PDF, 11.60 MB
IPFS:
,
english, 2022
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