Abstract
Information leakage in the medical industry has become an urgent problem to be solved in the field of Internet security. However, due to the need for automated or semiautomated authorization management for privacy protection in the big data environment, the traditional privacy protection model cannot adapt to this complex open environment. Although some scholars have studied the risk assessment model of privacy disclosure in the medical big data environment, it is still in the initial stage of exploration. This paper analyzes the key indicators that affect medical big data security and privacy leakage, including user access behavior and trust, from the perspective of users through literature review and expert consultation. Also, based on the user’s historical access information and interaction records, the user’s access behavior and trust are quantified with the help of information entropy and probability, and a definition expression is given explicitly. Finally, the entire experimental process and specific operations are introduced in three aspects: the experimental environment, the experimental data, and the experimental process, and then, the predicted results of the model are compared with the actual output through the 10-fold cross verification with Matlab. The results prove that the model in this paper is feasible. In addition, the method in this paper is compared with the current more classical medical big data risk assessment model, and the results show that when the proportion of illegal users is less than 15%, the model in this paper is more superior in terms of accuracy and recall.
Highlights
With the development of information technology, the era of big data has come quietly, bringing opportunities and different challenges to all walks of life
E rest of this paper is organized as follows: the second part discusses the research progress and current situation at home and abroad from the two aspects of medical big data security and privacy protection technology, risk-based access control, and summarizes the research status at home and abroad; the third part first introduces the relevant theories and principles, formalizes the definition of risk indicators, and combines fuzzy theory and a neural network to establish a risk quantification model based on adaptive neural fuzzy theory; the fourth part has carried out simulation experiments to prove that the model in this paper is feasible and efficient; and the fifth part mainly summarizes the work of this paper
From the perspective of the technology and method of privacy protection, it can be roughly divided into privacy protection technology based on anonymity and differential privacy; from the perspective of big data security technology, current research is mainly based on cryptography; from a management perspective, analysis can be summarized into the following two categories: one is the use of electronic information technology to monitor networks, platforms, and management systems; the other is the use of computer methods to analyze and mine medical data, such as machine learning
Summary
With the development of information technology, the era of big data has come quietly, bringing opportunities and different challenges to all walks of life. We can view the current status of the medical industry through a set of data from the United States: medical machinery without any security protection accounts for 77%, and medical equipment with a certain security strategy accounts for 27% Of these attacks, 17% came from medical equipment, and 75% of the traffic on the hospital’s LAN was not monitored and audited, and the hospital itself knew that patient privacy was leaking every day. The privacy leak rate of medical big data in China is slightly lower, personal privacy leaks occur from time to time, and there are currently no complete laws and regulations on personal privacy protection Medical data have their particularity, because their data source is mainly “people.” No matter what level of application, it involves human privacy and social stability [1]. E rest of this paper is organized as follows: the second part discusses the research progress and current situation at home and abroad from the two aspects of medical big data security and privacy protection technology, risk-based access control, and summarizes the research status at home and abroad; the third part first introduces the relevant theories and principles, formalizes the definition of risk indicators, and combines fuzzy theory and a neural network to establish a risk quantification model based on adaptive neural fuzzy theory; the fourth part has carried out simulation experiments to prove that the model in this paper is feasible and efficient; and the fifth part mainly summarizes the work of this paper
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