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Privacy-Preserving Machine Learning
J. Chang
(Autor)
·
Di Zhuang
(Autor)
·
G. Samaraweera
(Autor)
·
Manning Publications
· Tapa Blanda
Privacy-Preserving Machine Learning - Chang, J. ; Zhuang, Di ; Samaraweera, G.
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Reseña del libro "Privacy-Preserving Machine Learning"
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 forfacial 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. Large-scale scandals such as the Facebook Cambridge Analytic a data breach have made many users wary of sharing sensitive and personal information. Demand has surged among machine learning engineers for privacy-preserving techniques that can keep users private details secure without adversely affecting the performance of models.
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