Imperial College London
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Professor Hamed Haddadi

Faculty of Engineering, Department of Computing

Professor of Human-Centred Systems
 
 
 
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Contact

 

h.haddadi Website

 
 
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Location

 

2Translation & Innovation Hub BuildingWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Osia:2018:10.1109/MC.2018.2381113,
author = {Osia, SA and Shamsabadi, AS and Taheri, A and Rabiee, HR and Haddadi, H},
doi = {10.1109/MC.2018.2381113},
journal = {Computer},
pages = {42--49},
title = {Private and scalable personal data analytics using hybrid edge-to-cloud deep learning},
url = {http://dx.doi.org/10.1109/MC.2018.2381113},
volume = {51},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Osia,SA
AU  - Shamsabadi,AS
AU  - Taheri,A
AU  - Rabiee,HR
AU  - Haddadi,H
DO  - 10.1109/MC.2018.2381113
EP  - 49
PY  - 2018///
SN  - 0018-9162
SP  - 42
TI  - Private and scalable personal data analytics using hybrid edge-to-cloud deep learning
T2  - Computer
UR  - http://dx.doi.org/10.1109/MC.2018.2381113
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000433318900005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/60678
VL  - 51
ER  -
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