A medical big data access control model based on fuzzy trust prediction and regression analysis

Abstract

One of the important issues facing HIS (Hospital Information System) in the context of big data is how to ensure that massive data and resources are protected from internal attacks and reduce the abuse of medical information. However, the existing single-value quantitative access control model based on trust or risk may not well reflect the true trust or risk situation because it cannot describe the timeliness and trend of the quantitative value. In response to this problem, we propose an access control model based on the credibility of the requesting user. Quantify the trust based on the user’s historical visit records on the HIS, and introduce the user’s historical behavior trend into the trust evaluation model through the corresponding regression analysis model. Comparative experiments show that in predicting linear, geometric, exponential, and mixed trends, the regression model in this paper is better than existing methods in predicting trust accuracy and predicting trust trends. Different from the working system of trusted access control model proposed in the existing literature, the model in this paper adds “Behavior warning module (BWM)”. The working mode that “User-visit, Early-warning, Trust-evaluation, Access-control” is very effective in reducing information leakage caused by visitors with non-profit purposes (such as curiosity) and purposeless (such as habitual browsing). And this also has a positive effect on improving the overall behavior level of users in the system.