Privacy-Preserving Deep Neural Network Methods: Computational and Perceptual Methods—An Overview

Clicks: 237
ID: 265921
2021
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Abstract
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.
Reference Key
saj2021electronicsprivacy-preserving Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Raghida El Saj;Ehsan Sedgh Gooya;Ayman Alfalou;Mohamad Khalil;El Saj, Raghida;Sedgh Gooya, Ehsan;Alfalou, Ayman;Khalil, Mohamad;
Journal Electronics
Year 2021
DOI
10.3390/electronics10111367
URL
https://www.mdpi.com/2079-9292/10/11/1367
Keywords
deep neural networks cryptography privacy-preserving

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