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Privacy-Enhancing Aggregation Techniques for Smart Grid Communications: Wireless Networks

Autor Rongxing Lu
en Limba Engleză Hardback – 6 iun 2016
This book provides an overview of security and privacy issues in smart grid communications, as well as the challenges in addressing these issues. It also introduces several privacy enhancing aggregation techniques including multidimensional data aggregation, subset data aggregation, multifunctional data aggregation, data aggregation with fault tolerance, data aggregation with differential privacy, and data aggregation with integrity protection. 
Offering a comprehensive exploration of various privacy preserving data aggregation techniques, this book is an exceptional resource for the academics, researchers, and graduate students seeking to exploit secure data aggregation techniques in smart grid communications and Internet of Things (IoT) scenarios.

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Specificații

ISBN-13: 9783319328973
ISBN-10: 3319328972
Pagini: 158
Ilustrații: XVI, 177 p. 28 illus.
Dimensiuni: 155 x 235 x 13 mm
Greutate: 0.45 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seria Wireless Networks

Locul publicării:Cham, Switzerland

Cuprins

Introduction.- Homomorphic Public Key Encryption Techniques.- Privacy-Preserving Multidimensional Data Aggregation.- Privacy-Preserving Subset Data Aggregation.- Privacy-Preserving Multifunctional Data Aggregation.- Privacy-Preserving Data Aggregation with Fault Tolerance.- Differentially Private Data Aggregation with Fault Tolerance.- Privacy-Preserving Data Aggregation with Data Integrity and Fault Tolerance.


Caracteristici

Treats both theoretical and practical aspects of privacy-enhancing data aggregation in smart grid communications Maximize reader insight into privacy-enhancing data aggregation techniques in IoT scenarios in general, and smart grid communications in particular Covers privacy-preserving multi-dimensional data aggregation, privacy-preserving multi-function data aggregation, and privacy-preserving data aggregation with fault tolerance and/or differential privacy Includes supplementary material: sn.pub/extras