Privacy-Preserving Machine Learning (Paperback)

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.

透過真實世界的使用案例，本書揭示了複雜的隱私增強技術，包括人臉識別、雲端數據存儲等。《隱私保護機器學習》是一本實用指南，教你如何保持機器學習數據的匿名性和安全性。你將學習不同隱私保護技術的核心原則，並將理論應用於自己的機器學習項目中。本書通過真實世界的使用案例，揭示了複雜的隱私增強技術，包括人臉識別、雲端數據存儲等。除了技術實施的技能外，你還將了解當前和未來的機器學習隱私挑戰，以及如何根據自己的需求適應技術。完成閱讀後，你將能夠創建能夠保護用戶隱私而不影響數據質量和模型性能的機器學習系統。購買紙質書籍還包括一本免費的電子書（PDF、Kindle和ePub格式），由Manning Publications提供。

J. Morris Chang is a professor in the Department of Electrical Engineering of University of South Florida, Tampa, USA. He received his PhD from North Carolina State University. Since 2012, his research projects on cybersecurity and machine learning have been funded by DARPA and agencies under DoD. He has led a DARPA project under the Brandeis Program, focusing on privacy-preserving computation over the internet for three years.

Di Zhuang received his BSc degree in computer science and information security from Nankai University, Tianjin, China. He is currently a PhD candidate in the Department of Electrical Engineering of University of South Florida, Tampa, USA. He conducted privacy-preserving machine learning research under the DARPA Brandeis Program from 2015 to 2018.

G. Dumindu Samaraweera received his BSc degree in computer systems and networking from Curtin University, Australia, and a MSc in enterprise application development degree from Sheffield Hallam University, UK. He is currently reading for his PhD in electrical engineering at University of South Florida, Tampa.

J. Morris Chang 是美國南佛羅里達大學電機工程系的教授。他在北卡羅來納州立大學獲得博士學位。自2012年以來，他的研究項目在DARPA和國防部的機構資助下，專注於網絡安全和機器學習。他曾領導一個DARPA項目，該項目在布蘭代斯計劃下進行，專注於互聯網上的隱私保護計算，持續三年。

Di Zhuang 在中國天津的南開大學獲得計算機科學和信息安全學士學位。他目前是美國南佛羅里達大學電機工程系的博士候選人。他在2015年至2018年期間，在DARPA布蘭代斯計劃下進行了隱私保護機器學習的研究。

G. Dumindu Samaraweera 在澳大利亞柯廷大學獲得計算系統和網絡學士學位，並在英國謝菲爾德哈勒姆大學獲得企業應用開發碩士學位。他目前正在美國南佛羅里達大學讀取電機工程的博士學位。