基于Hopfield神经网络的可重新配置的安全解决方案,用于电子医疗应用程序
C Lakshmi1, C Nithya2, K Thenmozhi3
1School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur, 613 401, India.
Scientific reports
|February 15, 2025
概括
本研究介绍了一种基于FPGA的新型加密方案,用于在电子医疗保健中保护医疗图像. 该方法使用霍普菲尔德神经网络 (HNN) 和流密码来实现强大的图像隐私和对攻击的弹性.
科学领域:
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
- 密码学 密码学 密码学 密码学
背景情况:
- 保护医疗图像对于电子医疗应用中的患者隐私至关重要.
- 现有的基于软件的加密方法可能无法满足实时医学成像的性能要求.
研究的目的:
- 为医疗图像提出一种新的,硬件实现的加密方案.
- 利用现场可编程门阵列 (FPGA) 提高电子医疗保健的安全性和性能.
主要方法:
- 一种双层加密技术,它结合了Hopfield神经网络 (HNN) 进行扩散和流密码用于混.
- 在可重新配置硬件 (FPGA) 上实现并行处理和速度.
- 使用特定图像的密钥来增强安全性.
主要成果:
- 该加密方案实现了对统计攻击的高弹性,平均度为7.99和接近零的相关性.
- 在FPGA的实施中,在处理速度和并行性方面表现出了显著的优势.
- 在英特尔Cyclone V FPGA上,FPGA实现消耗了20%的硬件资源和424.71兆瓦的功率.
结论:
- 拟议的基于FPGA的加密方案为电子医疗保健中的医疗图像保护提供了安全有效的解决方案.
- 通过FPGA进行硬件加速对于实时,资源有限的医学成像应用是有益的.
- 双层方法有效地提高了数据的机密性和完整性.
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