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Tytuł:
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A privacy preservation framework for feedforward-designed convolutional neural networks.
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Autorzy:
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Li; Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China; School of Computer Science and Engineering, Guangxi Normal University, Guilin, China.
Wang J; Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China; School of Computer Science and Engineering, Guangxi Normal University, Guilin, China. Electronic address: .
Li Q; School of Computer Science and Engineering, Guangxi Normal University, Guilin, China.
Hu Y; School of Computer Science and Engineering, Guangxi Normal University, Guilin, China.
Li X; Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, China; School of Computer Science and Engineering, Guangxi Normal University, Guilin, China. Electronic address: .
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Źródło:
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Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2022 Nov; Vol. 155, pp. 14-27. Date of Electronic Publication: 2022 Aug 10.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: New York : Pergamon Press, [c1988-
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MeSH Terms:
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Privacy*
Neural Networks, Computer*
Algorithms ; Cloud Computing
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Contributed Indexing:
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Keywords: Convolutional neural networks; Differential privacy; Feature selection; Feedforward-designed; Over-fitting
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Entry Date(s):
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Date Created: 20220826 Date Completed: 20221025 Latest Revision: 20221025
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Update Code:
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20240104
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DOI:
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10.1016/j.neunet.2022.08.005
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PMID:
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36027662
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A feedforward-designed convolutional neural network (FF-CNN) is an interpretable neural network with low training complexity. Unlike a neural network trained using backpropagation (BP) algorithms and optimizers (e.g., stochastic gradient descent (SGD) and Adam), a FF-CNN obtains the model parameters in one feed-forward calculation based on two methods of data statistics: subspace approximation with adjusted bias and least squares regression. Currently, models based on FF-CNN training methods have achieved outstanding performance in the fields of image classification and point cloud data processing. In this study, we analyze and verify that there is a risk of user privacy leakage during the training process of FF-CNN and existing privacy-preserving methods for model gradients or loss functions do not apply to FF-CNN models. Therefore, we propose a securely forward-designed convolutional neural network algorithm (SFF-CNN) to protect the privacy and security of data providers for the FF-CNN model. Firstly, we propose the DPSaab algorithm to add the corresponding noise to the one-stage Saab transform in the FF-CNN design for improved protection performance. Secondly, because noise addition brings the risk of model over-fitting and further increases the possibility of privacy leakage, we propose the SJS algorithm to filter the input features of the fully connected model layer. Finally, we theoretically prove that the proposed algorithm satisfies differential privacy and experimentally demonstrate that the proposed algorithm has strong privacy protection. The proposed algorithm outperforms the compared deep learning privacy-preserving algorithms in terms of utility and robustness.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022. Published by Elsevier Ltd.)
