@inproceedings{15ceb64302814d04b0a10b3fe92ca450,
title = "Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecurity-Based Approach",
abstract = "This paper presents a novel two-stage approach to enhance the quality and privacy of X-ray medical images. The first stage leverages generative adversarial networks (GANs) for effective denoising, eliminating noise and artifacts from X-ray images while improving the visibility of critical anatomical structures. Subsequently, number-Theoretic transform (NTT) polynomial multiplication is integrated with Kyber to accelerate the encryption and decryption of the denoised X-ray images. This encryption safeguards the privacy of sensitive patient data and provides resilience against potential quantum computing attacks, ensuring long-Term data security. Implementing Kyber-based encryption and decryption on a graphics processing unit (GPU) architecture significantly reduces latency, enabling real-Time and secure access to critical healthcare information.",
keywords = "GANs, Images denoising, Kyber, number theoretic transform",
author = "Phan, {Quoc Bao} and Linh Nguyen and Nguyen, {Tuy Tan} and Nguyen, {Dinh C.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Consumer Electronics, ICCE 2024 ; Conference date: 06-01-2024 Through 08-01-2024",
year = "2024",
doi = "10.1109/ICCE59016.2024.10444428",
language = "English (US)",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE International Conference on Consumer Electronics, ICCE 2024",
}