摘要
医疗中的隐私数据是医疗领域的核心资产,但是大多数医院都缺少应对外界和内部窃取数据操作的反制手段,医疗行业成为隐私泄露的一个重点行业。因此,提出一种基于风险访问控制的安全评估模型,该模型引入了用户信任值,能够提高模型的判断准确率,并且通过人工神经网络和模糊理论建立了模型,能够对用户的风险状况进行预测。实验结果表明,基于风险自适应的访问控制模型能够很好地应对用户数量过多的问题,并且在用户数量较低时,也能表现出较为良好的模型性能,基于自适应的神经模糊理论访问控制模型的不同科室评价分数分别为96.6、91.3、87.6、86.5。研究结果表明,提出的自适应神经模糊理论访问控制模型能够在医疗数据中针对不同用户的需求提供对应的权限,有效防止患者个人隐私数据的泄露,并且使得用户获取信息的效率得到最大化。
Privacy data in healthcare are a core asset in the healthcare industry,but most hospitals lack countermeasures against external and internal data theft operations,making the healthcare industry a key industry for privacy breaches.Therefore,this study proposes a security evaluation model based on risk access control,which introduces user trust values and can improve the accuracy of the model judgment.The model is also established through artificial neural networks and fuzzy theory,and can predict the user’s risk situation.The experimental results show that the risk adaptive access control model can effectively deal with the problem of excessive user numbers,and can also demonstrate good model performance even when the number of users is low.The evaluation scores of different departments based on the adaptive neural fuzzy theory access control model are 96.6,91.3,87.6,and 86.5,respectively.The research results indicate that the proposed adaptive neural fuzzy theory access control model can provide corresponding permissions for different user needs in medical data,effectively preventing the leakage of patient personal privacy data,and maximizing the efficiency of user access to information.
作者
宋宇轩
SONG Yuxuan(Beijing Geriatric Hospital,Beijing 100095,China;IoT system,Hunan Institute of Science and Technology,Yueyang 414006,China)
出处
《微型电脑应用》
2024年第4期202-204,208,共4页
Microcomputer Applications
关键词
医疗大数据
个人隐私
访问控制
模糊理论
神经网络
medical big data
personal privacy
access control
fuzzy theory
neural network
