Private and scalable personal data analytics using hybrid edge-to-cloud deep learning
File(s)paper.pdf (877.51 KB)
Accepted version
Author(s)
Osia, Seyed Ali
Shamsabadi, Ali Shahin
Taheri, Ali
Rabiee, Hamid R
Haddadi, Hamed
Type
Journal Article
Abstract
Although the ability to collect, collate, and analyze the vast amount of data generated from cyber-physical systems and Internet of Things devices can be beneficial to both users and industry, this process has led to a number of challenges, including privacy and scalability issues. The authors present a hybrid framework where user-centered edge devices and resources can complement the cloud for providing privacy-aware, accurate, and efficient analytics.
Date Issued
2018-05-01
Date Acceptance
2018-05-01
Citation
Computer, 2018, 51 (5), pp.42-49
ISSN
0018-9162
Publisher
IEEE
Start Page
42
End Page
49
Journal / Book Title
Computer
Volume
51
Issue
5
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000433318900005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
EP/N028260/2
Subjects
Science & Technology
Technology
Computer Science, Hardware & Architecture
Computer Science, Software Engineering
Computer Science
Publication Status
Published
Date Publish Online
2018-05-24