Privacy-Preserving Machine Learning (Manning)

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.

J. Morris Chang, Di Zhuang, and G. Dumindu Samaraweera

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.

*Don’t forget you can get 35% off with your Devtalk discount! Just use the coupon code “” at checkout :+1:

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