Abstract
Edge computing is playing an increasingly important role in the field of health care. Edge computing provides high-quality personalized services to patients based on user and device data information. However, edge nodes will collect a large amount of sensitive patient information, and patients will also bear the risk of privacy disclosure while enjoying personalized services. How to reduce the risk of privacy disclosure while ensuring that patients enjoy personalized services brought by edge computing is the research content of this paper. In this paper, the work flow and management mode of Hospital Information System (HIS) are investigated on the spot, and the risk-adaptive access control model based on entropy is established. First, we use International Classification of Diseases, Tenth Revision (ICD-10) to mark the information resources accessed by users and use information entropy to measure the correlation “α” between medical information accessed by users and work tasks. Finally, we analyze the relationship between correlation “α” and risk through an example. The results show that users with high correlation α have low risk of access behavior, and users with low risk have high correlation α of access information resources and work goals. This discovery can help managers predict users’ access behavior in the Big Data environment, so as to dynamically formulate access control policies according to the actual access situation of users and then realize the privacy protection of medical big health data.
Highlights
Edge computing is used to extend cloud computing to the edge of the Web
Inspired by previous studies [4–6], this paper proposes a medical Big Data privacy protection model based on risk-adaptive access control. e main contributions are as follows: (i) We build a set of diagnostic codes that users can access under specific work goals
Workflow and Authority Management Mode in Hospital Information System (HIS). rough the field investigation of HIS in some hospitals in Kunming, we found that the system generally includes four main modules: outpatient workflow, inpatient workflow, permission allocation, and drug storehouse, while the first three modules are mainly involved in the study of medical Big Data privacy issues
Summary
Edge computing is used to extend cloud computing to the edge of the Web. edge computing is a new distributed computing mode, in which multiple edge nodes located between cloud servers and local users cooperate to complete outsourced storage and computing tasks [1]. The research of edge computing combined with medical scenarios mainly focuses on the design of network communication protocol and routing algorithm but neglects the research of information security and privacy protection in medical scenarios. Access control technology has become a hotspot of current research, but it mainly targets at the field of operating system, and there are not many researches in the information field, especially the research on the security and privacy protection of medical Big Data. Inspired by previous studies [4–6], this paper proposes a medical Big Data privacy protection model based on risk-adaptive access control.
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