Protecting Privacy through Homomorphic Encryption (Hardcover, 1st ed. 2021)

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 this important 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 Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology. A clear picture of how homomorphic encryption works, how to use it to solve real-world problems, and how to efficiently strengthen privacy protection, will naturally become clear.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Release date:	2022
First published:	2021
Editors:	Kristin Lauter • Wei Dai • Kim Laine
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	176
Edition:	1st ed. 2021
ISBN-13:	978-3-03-077286-4
Categories:	Books > Reference & Interdisciplinary > Communication studies > Coding theory & cryptology Books > Computing & IT > Social & legal aspects of computing > Privacy & data protection Books > Science & Mathematics > Mathematics > Geometry > Algebraic geometry Books > Science & Mathematics > Mathematics > Number theory > General Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Computing & IT > Applications of computing > Databases > Data security & data encryption
LSN:	3-03-077286-1
Barcode:	9783030772864

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