EHKP-Res: an explainable dynamic security solution for medical healthcare✩

Xiaoyan Chen; Jiahong Cai; Weidong Xiao; Yingzi Huo; Jin Wang; Wei Liang

doi:10.1016/j.dcan.2024.11.006

›› 2026, Vol. 12 ›› Issue (3) :462 -471. DOI: 10.1016/j.dcan.2024.11.006

Research Article

research-article

EHKP-Res: an explainable dynamic security solution for medical healthcare^✩

Xiaoyan Chen ^a
, Jiahong Cai ^b^,^c
, Weidong Xiao ^a
, Yingzi Huo ^b^,^c
, Jin Wang ^b^,^c
, Wei Liang ^b^,^c^,^*

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^a School of Software Engineering, Xiamen University of Technology , Xiamen 361024, China

^b School of Computer Science and Engineering, Hunan University of Science and Technology , Xiangtan 411201, China

^c Hunan Key Laboratory for Service Computing and Novel Software Technology , Xiangtan 411201, China

^* E-mail address: wliang@hnust.edu.cn (W. Liang).

Xiaoyan Chen received the M.S. degree in Department of Software Engineering from the Xiamen University in 2012. She is an associate professor of the School of Software Engineering, Xiamen University of Technology. Her research interests include blockchain security technology, Big Data Analysis, networks security protection, embedded system and hardware IP protection, and security management in wireless sensor networks (WSN).

Jiahong Cai is a Ph.D. candidate at the School of Computer Science and Engineering, Hunan University of Science and Technology, China. He has published several high-quality peer-reviewed papers in journals and conferences. His research interests include blockchain security, deep reinforcement learning, and the Internet of Vehicles.

Weidong Xiao is a Professor at School of software Engineering, Xiamen University of Technology, He received the Bachelor degree from Jiangxi Normal University in 1992, he received his M.S. degree at Xiamen University in 2003. He has published more than 20 referred journal and conference papers. His research interests include Networks Security Protection, embedded system and Hardware/IP protection, and Fog computing, and Security management in WSN.

Yingzi Huo is a post-undergraduate at the School of Computer Science and Engineering, Hunan University of Science and Technology, China. She has published several high-quality peer-reviewed papers in journals and conferences. Her research interests include the Internet of Vehicles, blockchain network and identity authentication.

Jin Wang received the B.S and M.S. degree from Nanjing University of Posts and Telecommunications, China in 2002, 2005 respectively. He received Ph.D. degree from Kyung Hee University Korea in 2010. Now, he is a professor at Hunan University of Science and Technology. He has published more than 400 international journal and conference papers. His research interests mainly include wireless ad hoc and sensor network, network performance analysis and optimization etc. He is a Fellow of IET and a senior member of the IEEE.

Wei Liang received a Ph.D. degree in computer science and technology from Hunan University in 2013, and was a Postdoctoral Scholar at Lehigh University from 2014 to 2016. Dr. Liang is currently a Professor at the School of Computer/School of Software, University of South China. He has authored or co-authored more than 150 journal/conference papers such as IEEE TC, TIFS, TSC, TITS, TII, TETC, TCBB, and IoTJ. His research interests include intelligent transportation, security of IoV, blockchain, and embedded system.

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Abstract

An increasing number of medical institutions and patients are adopting the practice of storing their data on medical cloud servers, which greatly facilitates the sharing of medical data. However, when many nodes are connected to the medical big data architecture, the overall system’s security can be compromised. To address this, an explainable dynamic security protection scheme for medical healthcare, named EHKP-Res, has been proposed. This scheme aggregates data relevance features and integrates them with a deep residual network to predict the behavior of medical staff, addressing the issue of sudden changes in their behavioral credibility. To tackle the black-box problem, a model post-interpretation scheme using Bayesian networks to generate perturbed datasets has been proposed. It calculates sample weights and performs nonlinear fitting, deriving model interpretation through the eigenvalues of the instances. Experimental results show that the proposed Hidden KP-ABE method reduces time overhead by 14.95% compared to other access control methods and achieves a prediction accuracy of 98.34% for doctor trust metrics, effectively preventing malicious behavior by doctors.

Keywords

Attribute-based encryption / Behavior prediction / Deep Residual Network (ResNet) / Explainable artificial intelligence / Local Interpretable Model-agnostic Explanations (LIME)

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Xiaoyan Chen, Jiahong Cai, Weidong Xiao, Yingzi Huo, Jin Wang, Wei Liang. EHKP-Res: an explainable dynamic security solution for medical healthcare^✩. , 2026, 12 (3) : 462-471 DOI:10.1016/j.dcan.2024.11.006

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CRediT authorship contribution statement

Xiaoyan Chen: Writing – original draft, Methodology, Investigation. Jiahong Cai: Writing – original draft, Software, Resources. Weidong Xiao: Writing – review & editing, Formal analysis. Yingzi Huo: Writing – original draft, Visualization, Methodology. Jin Wang: Writing – review & editing, Funding acquisition. Wei Liang: Writing – review & editing, Funding acquisition.

Declaration of competing interest

No potential conflict of interest was reported by the authors.

Acknowledgements

This work was supported in part by the Natural Science Foundation of Fujian Province under Grant 2023J011460, in part by the National Natural Science Foundation of China under Grant 62072170 and Grant 62072056, and in part by the Key Project of Hunan Provincial Natural Science Foundation under Grant 2024JJ3017.

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