Privacy-Preserving X-ray Image Enhancement: A GAN-Cybersecurity-Based Approach

Quoc Bao Phan, Linh Nguyen, Tuy Tan Nguyen, Dinh C. Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2024 IEEE International Conference on Consumer Electronics, ICCE 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350324136
DOIs
StatePublished - 2024
Externally publishedYes
Event2024 IEEE International Conference on Consumer Electronics, ICCE 2024 - Las Vegas, United States
Duration: Jan 6 2024Jan 8 2024

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
ISSN (Print)0747-668X
ISSN (Electronic)2159-1423

Conference

Conference2024 IEEE International Conference on Consumer Electronics, ICCE 2024
Country/TerritoryUnited States
CityLas Vegas
Period1/6/241/8/24

Keywords

  • GANs
  • Images denoising
  • Kyber
  • number theoretic transform

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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