An access control model for medical big data based on clustering and risk

Abstract

Access control has been widely adopted by distributed platforms, and its effectiveness is of great importance to the quality of services provided by such platforms. However, traditional access control is difficult to apply to scenarios where authorization changes frequently and to extremely large-scale datasets with limited resources. This paper proposes an access control model based on spectral clustering (SC) and risk (SC-RBAC), which is more suitable for big data medical scenarios. Based on user history access data, an improved SC algorithm is used to cluster doctor users. Then, the user classification is introduced as a parameter into the information entropy to improve the accuracy of quantifying the user’s access behavior risk. Finally, based on the accurate risk value of access behavior, we assign access rights to users through the access control function constructed in the paper. Experimental results show that in three different situations, the model proposed in this paper can distinguish the two types of doctors well, the accuracy of the model can reach more than 90%, and it outperforms other access control models.