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

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.