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De-correlation neural network for synchronous implementation of estimation and secrecy
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Guo, Canyang, Chen, Chi-Hua, Chang, Ching-Chun, Hwang, Feng-Jang and Chang, Chin-Chen (2023) De-correlation neural network for synchronous implementation of estimation and secrecy. IEEE Communications Letters, 27 (1). pp. 165-169. doi:10.1109/lcomm.2022.3215697 ISSN 1089-7798.
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Official URL: https://doi.org/10.1109/LCOMM.2022.3215697
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).
Item Type: | Journal Article | ||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Journal or Publication Title: | IEEE Communications Letters | ||||||
Publisher: | IEEE | ||||||
ISSN: | 1089-7798 | ||||||
Official Date: | January 2023 | ||||||
Dates: |
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Volume: | 27 | ||||||
Number: | 1 | ||||||
Page Range: | pp. 165-169 | ||||||
DOI: | 10.1109/lcomm.2022.3215697 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
RIOXX Funder/Project Grant: |
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