Construction of Medical Data Security Evaluation Model Based on Adaptive Neural Fuzzy Theory
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 pre-dict 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 pa-tient personal privacy data,and maximizing the efficiency of user access to information.
medical big datapersonal privacyaccess controlfuzzy theoryneural network
万方数据
维普
