Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview
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CRSI Research Center, Faculty of Engineering, Lebanese University, 1300 Tripoli, Lebanon
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[email protected], LSL Team, Yncrea Ouest, 29200 Brest, France
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Author to whom correspondence should be addressed.
Academic Editor: Stefanos Kollias
Electronics 2021, 10(11), 1367; https://doi.org/10.3390/electronics10111367
Received: 20 April 2021
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Revised: 10 May 2021
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Accepted: 18 May 2021
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Published: 7 June 2021
(This article belongs to the Special Issue Compressive Optical Image Encryption)
Privacy-preserving deep neural networks have become essential and have attracted the attention of many researchers due to the need to maintain the privacy and the confidentiality of personal and sensitive data. The importance of privacy-preserving networks has increased with the widespread use of neural networks as a service in unsecured cloud environments. Different methods have been proposed and developed to solve the privacy-preserving problem using deep neural networks on encrypted data. In this article, we reviewed some of the most relevant and well-known computational and perceptual image encryption methods. These methods as well as their results have been presented, compared, and the conditions of their use, the durability and robustness of some of them against attacks, have been discussed. Some of the mentioned methods have demonstrated an ability to hide information and make it difficult for adversaries to retrieve it while maintaining high classification accuracy. Based on the obtained results, it was suggested to develop and use some of the cited privacy-preserving methods in applications other than classification.
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Keywords:
privacy-preserving; deep neural networks; cryptography
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MDPI and ACS Style
El Saj, R.; Sedgh Gooya, E.; Alfalou, A.; Khalil, M. Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview. Electronics 2021, 10, 1367. https://doi.org/10.3390/electronics10111367
AMA Style
El Saj R, Sedgh Gooya E, Alfalou A, Khalil M. Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview. Electronics. 2021; 10(11):1367. https://doi.org/10.3390/electronics10111367
Chicago/Turabian StyleEl Saj, Raghida, Ehsan Sedgh Gooya, Ayman Alfalou, and Mohamad Khalil. 2021. "Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview" Electronics 10, no. 11: 1367. https://doi.org/10.3390/electronics10111367
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