双忆阻类脑混沌神经网络及其在IoMT数据隐私保护中应用

蔺海荣; 段晨星; 邓晓衡; GeyongMin

doi:10.11999/JEIT241133

双忆阻类脑混沌神经网络及其在IoMT数据隐私保护中应用

doi: 10.11999/JEIT241133 cstr: 32379.14.JEIT241133

1.
中南大学电子信息学院长沙 410083
2.
the Department of Computer Science, University of Exter, Exeter, EX4 4QF UK

基金项目: 国家自然科学基金(62201204, 62373372)，湖南省自然科学基金(2023JJ40168, 2023JJ30696)

详细信息

作者简介:
蔺海荣：男，副教授，研究方向为混沌密码学、隐私保护、忆阻神经网络、类脑计算

段晨星：男，硕士生，研究方向为混沌密码学、深度学习

邓晓衡：男，教授，研究方向为边缘计算、物联网、深度学习、网络与信息安全

GeyongMin：男，教授，研究方向为无线网络与边缘计算、大数据处理、数据孪生

通讯作者:
邓晓衡　dxh@csu.edu.cn

中图分类号: TN918.4; TP309.7
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- 被引次数: 0
出版历程
- 收稿日期: 2024-12-25
- 修回日期: 2025-04-11
- 网络出版日期: 2025-04-11
- 刊出日期: 2025-07-22

Dual-Memristor Brain-Like Chaotic Neural Network and Its Application in IoMT Data Privacy Protection

1.
School of Electronic Information, Central South University, Changsha 410083, China
2.
the Department of Computer Science, University of Exter, Exeter, EX4 4QF UK

Funds: The National Natural Science Foundation of China (62201204, 62373372), The Natural Science Foundation of Hunan (2023JJ40168, 2023JJ30696)

摘要

摘要: 近年来，医疗数据泄露频发，严重威胁患者隐私与健康安全，亟需有效的解决方案以保护医疗数据在传输过程中的隐私与安全性。该文提出了一种基于双忆阻类脑混沌神经网络的医疗物联网(Internet of Medical Things, IoMT)数据隐私保护方法，以应对这一挑战。首先，利用忆阻器的突触仿生特性，构建了一种基于Hopfield神经网络的双忆阻类脑混沌神经网络模型，并通过分岔图、Lyapunov指数谱、相图、时域图及吸引盆等非线性动力学工具，深入揭示了模型的复杂混沌动力学特性。研究结果表明，该网络不仅展现出复杂的网格多结构混沌吸引子特性，还具有平面初值位移调控能力，从而显著增强了其密码学应用潜力。为了验证其实用性与可靠性，基于微控制器单元(MCU)搭建了硬件平台，并通过硬件实验进一步确认了模型的复杂动力学行为。基于此模型，该文设计了一种结合双忆阻类脑混沌神经网络复杂混沌特性的高效IoMT数据隐私保护方法。在此基础上，对彩色医疗图像数据的加密效果进行了全面的安全性分析。实验结果表明，该方法在关键性能指标上表现优异，包括大密钥空间、低像素相关性、高密钥敏感性，以及对噪声与数据丢失攻击的强鲁棒性。该研究为IoMT环境下的医疗数据隐私保护提供了一种创新且有效的解决方案，为未来的智能医疗安全技术发展奠定了坚实基础。
- 忆阻器 /
- 混沌系统 /
- Hopfield神经网络 /
- 多吸引子 /
- 混沌加密
Abstract: Objective In recent years, frequent breaches of medical data have posed significant threats to patient privacy and health security, highlighting the urgent need for effective solutions to protect medical data privacy and security during transmission. This paper proposes a novel data privacy protection method for the Internet of Medical Things (IoMT) based on a dual-memristor-inspired brain-like chaotic neural network to address this challenge. Methods Leveraging the synaptic bionic characteristics of memristors, a dual-memristor brain-like chaotic neural network model based on the Hopfield neural network is developed. The complex chaotic dynamics of this model are thoroughly analyzed using nonlinear dynamics tools, including bifurcation diagrams, Lyapunov exponent spectra, phase portraits, time-domain waveforms, and basins of attraction. To validate its practicality and reliability, a hardware platform is created using a Microcontroller Unit (MCU), and hardware experiments confirm the model’s complex dynamic behaviors. Based on this model, an efficient IoMT data privacy protection method is designed by utilizing the complex chaotic properties of the dual-memristor brain-like chaotic neural network. A comprehensive security analysis of the encryption of colored medical image data is also performed. Results and Discussions The results demonstrate that the proposed network not only exhibits complex grid-like multi-structure chaotic attractors but also possesses the capability to regulate planar initial condition displacements, significantly enhancing its potential for cryptographic applications. Experimental findings indicate that this method performs exceptionally well across key metrics, including a large key space, low pixel correlation, high key sensitivity, and strong robustness against noise and data loss attacks. Conclusions This study presents an innovative and effective solution for protecting medical data privacy in IoMT environments, providing a solid foundation for the development of secure technologies in intelligent healthcare systems.
- Memristor /
- Chaotic system /
- Hopfield neural network /
- Multi-attractors /
- Chaotic encryption

HTML全文

图 1 忆阻器特征

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图 2 双忆阻Hopfield神经网络拓扑连接结构图

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图 3 随忆阻突触耦合强度(ρ_1, ρ₂)变化的动力学行为

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图 4 具有不同数目结构的$\varphi_1 $单方向多结构吸引子行为

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图 5 具有不同数目结构的$\varphi_2 $单方向多结构吸引子行为

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图 6 网格多结构混沌吸引子

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图 7 随忆阻突触初值($\varphi_{20} $)变化的动力学行为

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图 8 随忆阻突触初值($\varphi_{10} $)变化的动力学行为

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图 9 随忆阻突触初值($\varphi_{10} $, $\varphi_{20} $)变化的动力学行为

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图 10 基于微控制器的硬件平台和观察到的$\varphi_{1} $方向上多结构吸引子

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图 11 在示波器上观察到的$\varphi_1 $方向上，$\varphi_2 $方向上和网格多结构的混沌吸引子

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图 12 基于双忆阻类脑混沌神经网络的IoMT数据隐私保护方法

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图 13 医疗图像加密测试结果

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图 14 密钥敏感性测试结果

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图 15 噪声与数据丢失攻击测试结果

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表 1 忆阻突触控制参数与网格吸引子关系

N₁/M₁	$\varphi_1 $方向(n)	N₂/M₂	$\varphi_2 $方向(m)
0	1/2	0	1/2
1	3/4	1	3/4
2	5/6	2	5/6
…	…	…	…
N1/M1	(2N1+1)/(2M1+2)	N2/M2	(2N2+1)/(2M2+2)

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表 2 不同加密方法的密钥空间比较

参考文献	密钥空间
文献[26]	2⁵⁰⁴
文献[31]	2¹⁰⁵
文献[34]	2¹⁴⁴
文献[36]	2⁴⁵⁶
本文	2⁷⁶⁸

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表 3 原图像与加密图像的相关系数和信息熵

		相关系数	信息熵		差分攻击
图像		垂直/水平/对角线	RGB	红R/绿G/蓝B	NPCR/UACI
大脑	原图	0.946871/0.944926/0.934295	5.5487	6.4159/4.9046/4.9319	99.6907/32.8161
大脑	加密图	–0.0113173/0.006627/–0.017526	7.9996	7.9993/7.9992/7.9993	99.6907/32.8161
红细胞	原图	0.905427/0.919528/0.869007	7.7647	6.9389/76146/7.6832	99.5060/34.6095
红细胞	加密图	0.016132/0.006560/0.007012	7.9998	7.9993/7.9993/7.9994	99.5060/34.6095
视网膜	原图	0.691344/0.714074/0.640713	6.1917	6.0691/6.4005/5.8215	99.5505/34.7912
视网膜	加密图	–0.006482/0.005781/0.011470	7.9997	7.9993/7.9993/7.9994	99.5505/34.7912
病毒	原图	0.961282/0.962064/0.945707	5.2211	5.0190/4.5569/5.0514	99.7292/37.3502
病毒	加密图	–0.015224/–0.001286/–0.010731	7.9996	7.9994/7.9994/7.9993	99.7292/37.3502

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表 4 不同彩色图像加密方法的信息熵比较

文献	信息熵
文献[9]	7.9994
文献[12]	7.9994
文献[26]	7.9977
文献[36]	7.9896
本文	7.9998

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表 5 不同加密方法的密钥敏感性比较

文献	密钥敏感度
文献[26]	10^–4
文献[31]	10^–10
文献[34]	10^–15
文献[36]	10^–16
本文	10^–16

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表 6 不同加密方法的抵抗噪声与数据丢失攻击能力比较

文献	抵抗噪声与数据丢失
文献[5]	无
文献[26]	无
文献[29]	无
本文	有

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表 7 NIST测试

统计测试	通过率	P值	结果
频率测试	0.99	0.474	通过
块内频数测试	0.98	0.596	通过
动向测试	1.00	0.419	通过
最大游程测试	1.00	0.718	通过
二进制矩阵秩测试	0.98	0.384	通过
频谱测试	1.00	0.868	通过
非重叠字匹配测试	0.99	0.637	通过
重叠字匹配测试	0.97	0.249	通过
毛勒通用统计测试	0.99	0.141	通过
线性复杂度测试	0.99	0.485	通过
系列测试	0.98	0.356	通过
近似熵测试	1.00	0.299	通过
累积测试	0.97	0.323	通过
随机游程测试	0.99	0.759	通过
随机游程变量测试	0.97	0.367	通过

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