| 書目名稱 | Protecting Privacy through Homomorphic Encryption | | 編輯 | Kristin Lauter,Wei Dai,Kim Laine | | 視頻video | http://file.papertrans.cn/762/761580/761580.mp4 | | 概述 | Presents many applications of homomorphic encryption for privacy protection, covering a broad range of fields.Includes the most recent research results known in the literature as well as time-tested c | | 圖書封面 |  | | 描述 | This book summarizes recent inventions, provides guidelines and recommendations, and demonstrates many practical applications of homomorphic encryption. This collection of papers represents the combined wisdom of the community of leading experts on Homomorphic Encryption. In the past 3 years, a global community consisting of researchers in academia, industry, and government, has been working closely to standardize homomorphic encryption. This is the first publication of whitepapers created by these experts that comprehensively describes the scientific inventions, presents a concrete security analysis, and broadly discusses applicable use scenarios and markets. This book also features a collection of privacy-preserving machine learning applications powered by homomorphic encryption designed by groups of top graduate students worldwide at the Private AI Bootcamp hosted by Microsoft Research..The volume aims to connect non-expert readers with thisimportant new cryptographic technology in an accessible and actionable way. Readers who have heard good things about homomorphic encryption but are not familiar with the details will find this book full of inspiration. Readers who have precon | | 出版日期 | Book 2021 | | 關(guān)鍵詞 | whitepapers; privacy protection; cryptography; technology developments; computer science applications; op | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-77287-1 | | isbn_softcover | 978-3-030-77289-5 | | isbn_ebook | 978-3-030-77287-1 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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