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Updated: May 27, 2025

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Reconfigurable security solution based on hopfield neural network for e-healthcare applications.

C Lakshmi1, C Nithya2, K Thenmozhi3

  • 1School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur, 613 401, India.

Scientific Reports
|February 15, 2025
PubMed
Summary

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This study introduces a novel FPGA-based encryption scheme for securing medical images in e-healthcare. The method uses a Hopfield Neural Network (HNN) and stream cipher for robust image privacy and resilience against attacks.

Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Cryptography

Background:

  • Securing medical images is critical for patient privacy in e-healthcare applications.
  • Existing software-based encryption methods may not meet the performance demands of real-time medical imaging.

Purpose of the Study:

  • To propose a novel, hardware-implemented encryption scheme for medical images.
  • To leverage Field-Programmable Gate Arrays (FPGAs) for enhanced security and performance in e-healthcare.

Main Methods:

  • A dual-layer encryption technique combining a Hopfield Neural Network (HNN) for diffusion and a stream cipher for confusion.
  • Implementation on reconfigurable hardware (FPGA) for parallel processing and speed.
  • Utilizing an image-specific key for enhanced security.
Keywords:
EncryptionFPGAHopfield Neural Network (HNN)Parallelism

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Main Results:

  • The encryption scheme achieved high resilience against statistical attacks, with an average entropy of 7.99 and near-zero correlation.
  • FPGA implementation demonstrated significant advantages in processing speed and parallelism.
  • The FPGA implementation consumed 20% of hardware resources and 424.71 mW of power on an Intel Cyclone V FPGA.

Conclusions:

  • The proposed FPGA-based encryption scheme offers a secure and efficient solution for medical image protection in e-healthcare.
  • Hardware acceleration via FPGAs is beneficial for real-time, resource-constrained medical imaging applications.
  • The dual-layer approach effectively enhances data confidentiality and integrity.