Homomorphic Encryption and Applications

ISBN-13: 9783319122281 / Angielski / Miękka / 2014 / 126 str.

Xun Yi; Russell Paulet; Elisa Bertino

Opis

This book introduces the fundamental concepts of homomorphic encryption. From these foundations, applications are developed in the fields of private information retrieval, private searching on streaming data, privacy-preserving data mining, electronic voting and cloud computing. The content is presented in an instructional and practical style, with concrete examples to enhance the reader's understanding. This volume achieves a balance between the theoretical and the practical components of modern information security. Readers will learn key principles of homomorphic encryption as well as their application in solving real world problems.

Recenzje

"This brief, 126-page book comprehensively presents various homomorphic encryption algorithms and their applications nicely. ... I would recommend this book as a reference for senior undergraduate and graduate students. Researchers and practitioners working in cryptography will also find it useful." (S. Ramakrishnan, Computing Reviews, May, 2015)

"This book presents the basic homomorphic encryption techniques and their applications. ... It is best suited for undergraduate students in computer science or mathematics with a basic background in cryptography ... . Overall, I can recommend this book as a general reference for possible independent study." (Burkhard Englert, computing Reviews, April, 2015)

"This book presents basic homomorphic encryption techniques and applications. ... This book is designed for undergraduate- or graduate-level courses in computer science or mathematics, as a general introduction suitable for self-study, and as a reference for students, researchers, and practitioners." (Jörg Desel, zbMATH 1308.68014, 2015)

Spis treści

Introduction.- Homomorphic Encryption.- Fully Homomorphic Encryption.- Remote End-to-End Voting Scheme.- Nearest Neighbor Queries with Location Privacy.- Private Searching on Streaming Data.