Research On Access Control Model Of Electronic Medical Records Based On Intuitionistic Fuzzy Trust

During the 14 th Five-Year Plan period,as the informatization of medicine develops and the new generation of information technology is integrated into medical services,the path of medical informatization in China becomes more and more apparent.Electronic medical records,the main product of medical informatization,contain rich resources of clinical medical data,mainly from patients.The exchange and use of electronic medical records has greatly improved the quality and safety of health care and services.However,the development of EMR has only just begun,and there are many bottlenecks in the sharing of EMR.Of particular concern is the leakage of privacy data in electronic health records.Leakage of personal data in electronic health records can cause tremendous losses to patients and jeopardize the stability of society.Secure management of privacy data in electronic health records is essential to the widespread use of shared electronic health records.How to effectively manage the privacy data of electronic medical record and prevent it from being leaked in the use stage is the key issue of current research.Access control is an important data security measure to ensure that users have lawful and appropriate access to data resources.However,the traditional access control has many limitations,such as static and coarse-grained,which is difficult to meet the dynamic and complex medical environment.Therefore,based on the practical problems of electronic medical record access,this thesis combines trust and access control technology,and proposes an EMR access control model based on intuitionistic fuzzy trust.This thesis includes work on the following main points.1.To realize personalized access control,the model assumes two access scenarios:Diagnosis and Learning scenario.Two scenarios were used to refine the doctor’s access behavior,and more targeted access control was carried out.At the same time,a preliminary diagnostic auxiliary model based on patient similarity was constructed in the diagnosis scenario to reduce the inadvertent over-access caused by the doctor’s diagnosis bias.In the learning scenario,a case recommendation model based on doctors’ historical access preference was constructed to reduce the curious excessive access in the name of learning.The introduction of the two models enhances the effectiveness of access control.2.When quantifying doctors’ trust,the model combines intuitionistic fuzzy with trust,and describes the degree of doctors’ trust through intuitionistic fuzzy trust to reflect the uncertainty in the process of quantifying trust.In the case of trust aggregation,the model combines time window and time decay.The optimized method improves the accuracy of trust aggregation,and the results are more consistent with the actual regular.3.In the process of access control,the model provides responsive and flexible dynamic management of doctors’ access behavior through the intuitionistic fuzzy clustering algorithm based on relative entropy and the reward and punishment mechanism.The experimental findings indicate that the working model in this study outperforms traditional models in identifying excessive doctor use and controlling its effects.