Privacy-Preserving Machine Learning (Paperback)
暫譯: 隱私保護的機器學習 (平裝本)
Chang, J. Morris, Zhuang, Di, Samaraweera, G. Dumindu
- 出版商: Manning
- 出版日期: 2023-04-21
- 定價: $2,150
- 售價: 8.8 折 $1,892 (限時優惠至 2025-03-31)
- 語言: 英文
- 頁數: 343
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1617298042
- ISBN-13: 9781617298042
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相關分類:
Machine Learning
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商品描述
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.
商品描述(中文翻譯)
複雜的隱私增強技術透過實際案例進行解密，包括臉部識別、雲端數據儲存等。
隱私保護機器學習 是一本實用指南，旨在保持機器學習數據的匿名性和安全性。您將學習不同隱私保護技術背後的核心原則，以及如何將理論應用於自己的機器學習實踐中。
複雜的隱私增強技術透過實際案例進行解密，包括臉部識別、雲端數據儲存等。除了技術實施的技能外，您還將了解當前和未來的機器學習隱私挑戰，以及如何根據您的特定需求調整技術。完成本書後，您將能夠創建保護用戶隱私的機器學習系統，而不會犧牲數據質量和模型性能。
購買印刷版書籍可獲得 Manning Publications 提供的免費電子書，格式包括 PDF、Kindle 和 ePub。
作者簡介
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.
作者簡介(中文翻譯)
張忠謀 (J. Morris Chang) 是美國南佛羅里達大學 (University of South Florida) 電機工程系的教授。他在北卡羅來納州立大學 (North Carolina State University) 獲得博士學位。自2012年以來，他的網路安全 (cybersecurity) 和機器學習 (machine learning) 研究專案已獲得國防高級研究計畫局 (DARPA) 及國防部 (DoD) 相關機構的資助。他曾主導一個DARPA專案，專注於網路上的隱私保護計算 (privacy-preserving computation)，並持續三年。
莊迪 (Di Zhuang) 在中國天津南開大學 (Nankai University) 獲得計算機科學與信息安全 (computer science and information security) 的學士學位。目前他是美國南佛羅里達大學 (University of South Florida) 電機工程系的博士候選人。他在2015年至2018年間參與了DARPA Brandeis計畫下的隱私保護機器學習研究。
G. Dumindu Samaraweera 在澳大利亞科廷大學 (Curtin University) 獲得計算機系統與網路 (computer systems and networking) 的學士學位，並在英國謝菲爾德哈倫大學 (Sheffield Hallam University) 獲得企業應用開發 (enterprise application development) 的碩士學位。目前他正在美國南佛羅里達大學 (University of South Florida) 進行電機工程的博士研究。
