Research On The Privacy Protection Model Of Healthcare Big-data For Insider Threats

Under the promotion of the state,the status of health and medical big data at the strategic level has steadily improved,and all walks of life have paid more attention to health and medical big data.At the same time,the potential benefits of health and medical big data have made data security an important The new challenge,how to protect personal privacy in health and medical big data from being leaked,will become a new academic focus.As the main source and storage point of health care big data in hospitals,how to protect the privacy of patients stored in the medical management system is the top priority.On the basis of referring to existing research,this paper establishes two models to solve this problem.The model in Chapter 3 solves the problem of how to measure the risk of user behavior,while Chapter 4 takes the model in Chapter 3 as the core and builds a health care big data privacy protection model for insider threats,the brief descriptions of two models as follows:1.Risk quantification method and rationing model of health and medical big data.The model introduces the concepts of risk,trust,currency and tolerance in the financial field,and uses risk and trust as quantitative indicators for the model to evaluate user access requests.At the same time,in order to improve the work efficiency of users and shorten the waiting time after users send access requests,the model will periodically allocate a certain amount of risk to trusted users,which makes trusted users Users can first access data resources,and then accept the risk assessment of the access by the system,which is called "quick access".The experimental results show that it is necessary to introduce a "fast access" mechanism,and this model can significantly shorten the waiting time after users send access requests.The average single access response time of the improved model is only about 0.29 times that before the improvement,which significantly shortens the response time of a single access request.2.A privacy protection model for health and medical big data facing insider threats.Considering the sensitivity and professionalism of health and medical big data,it is also necessary to judge whether the user’s access complies with the principle of knowing the needs,so as to further formulate an accurate access control strategy.Through the analysis of the current similar models,it is concluded that a practical privacy protection model should have the characteristics of high-speed response,high identification and adaptive control.In the case of the introduction of the "fast access" mechanism,the model can meet the requirements of high-speed response.In order to solve these shortcomings,this paper proposes a privacy protection model for health and medical big data facing insider threats.The model has the characteristics of high-speed response,high recognition and adaptive control,and achieves these goals through three verification steps: identity verification,authority verification,and access qualification verification.Experiments show that the model proposed in this paper is effective,and the performance comparison with similar models shows that the performance index of the proposed model is 14% higher under the current experimental conditions.