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 Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology.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.

Title: Protecting Privacy through Homomorphic Encryption Author(s): Kristin Lauter, Wei Dai, Kim Laine Publisher: Springer Year: 2021 ISBN: 9783030772895; 9783030772871

 

Title:	Protecting Privacy through Homomorphic Encryption		Volume:
Author(s):	Kristin Lauter, Wei Dai, Kim Laine
Series:		Periodical:
Publisher:	Springer	City:	Cham
Year:	2022	Edition:
Language:	English	Pages (biblio ech):	192183
ISBN:	3030772861, 9783030772864	ID:	3180247
Time added:	2022-01-04 14:08:15	Time modified:	2022-01-05 03:01:33
Library:		Library issue:
Size:	3 MB (3513013 bytes)	Extension:	pdf