Privacy-preserving image processing with neural networks
Department of Computer Science, University of New South Wales, Australia.
Review Article
World Journal of Advanced Research and Reviews, 2023, 18(03), 1686-1693
Publication history:
Received on 10 May 2023; revised on 24 June 2023; accepted on 27 June 2023
Abstract:
As image data becomes increasingly central to modern computing and surveillance systems, protecting individual privacy in visual content has become a major concern. Privacy-preserving image processing techniques aim to enable image analytics while preventing the leakage of sensitive information. This paper surveys core methods including image anonymization, differential privacy, homomorphic encryption, federated learning, and edge computing. We also evaluate real-world applications and provide insights into the trade-offs between privacy, utility, and computational cost.
Keywords:
Neural Network, Image Processing, Privacy Encryption, Fully Homomorphic Encryption (FHE)
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Copyright information:
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0