De-Correlation Neural Network for Synchronous Implementation of Estimation and Secrecy

Guo, C; Chen, CH; Chang, CC; Hwang, FJ; Chang, CC

De-Correlation Neural Network for Synchronous Implementation of Estimation and Secrecy

Guo, C Chen, CH Chang, CC Hwang, FJ Chang, CC

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Communications Letters, 2023, 27, (1), pp. 165-169
Issue Date:: 2023-01-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Guo, C
dc.contributor.author	Chen, CH
dc.contributor.author	Chang, CC
dc.contributor.author	Hwang, FJ
dc.contributor.author	Chang, CC
dc.date.accessioned	2024-03-19T01:50:41Z
dc.date.available	2024-03-19T01:50:41Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Communications Letters, 2023, 27, (1), pp. 165-169
dc.identifier.issn	1089-7798
dc.identifier.issn	1558-2558
dc.identifier.uri	http://hdl.handle.net/10453/176893
dc.description.abstract	Intelligent equipment within the Internet of things (IoT) carries out massive, frequent, and persistent data communication, making privacy protection particularly critical. Different from regular encryption methods or neural networks, this study proposes a de-correlation neural network (DeCNN) which synchronously realizes the estimation and privacy protection by a comprehensive loss function. In addition, a two-stage learning algorithm is utilized for solution optimization and computation enhancement. The DeCNN is deployed in the deep fingerprint positioning, and the experimental results demonstrate that the proposed method decreases the maximal correlation coefficient between the transmission data and target data from 0.95 to 0.34 (and 0.13) when the positioning error reaches 1.31 m (and 2.88 m).
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Communications Letters
dc.relation.isbasedon	10.1109/LCOMM.2022.3215697
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4606 Distributed computing and systems software
dc.title	De-Correlation Neural Network for Synchronous Implementation of Estimation and Secrecy
dc.type	Journal Article
utslib.citation.volume	27
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2024-03-19T01:50:39Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	27
utslib.citation.issue	1

Abstract:

Intelligent equipment within the Internet of things (IoT) carries out massive, frequent, and persistent data communication, making privacy protection particularly critical. Different from regular encryption methods or neural networks, this study proposes a de-correlation neural network (DeCNN) which synchronously realizes the estimation and privacy protection by a comprehensive loss function. In addition, a two-stage learning algorithm is utilized for solution optimization and computation enhancement. The DeCNN is deployed in the deep fingerprint positioning, and the experimental results demonstrate that the proposed method decreases the maximal correlation coefficient between the transmission data and target data from 0.95 to 0.34 (and 0.13) when the positioning error reaches 1.31 m (and 2.88 m).

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http://hdl.handle.net/10453/176893