Medical Sports Data Privacy Protection Method Based on Legal Risk Control

Abstract

With the continuous development of computer and network technology, the amount of information storage in medical information system is more and more large, which is prone to the problem of privacy information leakage, resulting in irreparable harm. In order to solve the problem of privacy leakage in the medical environment, a new privacy rating method is proposed according to the actual situation of the medical environment. The big data technology is used to effectively mine, analyze, integrate, and reuse medical data, and a new improved model is proposed. At the same time, the medical information system applying the improved model is designed according to the complex actual needs. The purpose of this paper is to correctly understand the positive role of medical sports big data (BD) research in the medical field and standardize the behavior of medical staff. On the one hand, it can improve the safety awareness of patients and enhance the standardization of medical treatment environment. This paper will analyze the meaning and research status of medical data from the perspective of legal risk control, focus on the status quo and existing problems of medical sports data privacy protection, and put forward positive countermeasures and some practical solutions. The results show that the medical sports information data has certain regularity and particularity, ease to spread, and mining. Hospitals and medical staff should make the areas and items restricted by law clear, standardize their own behaviors, constantly sum up experience, and actively improve and modify relevant measures.

Assessment of the Contribution of Information Adversarial Technology to Educational Development in the Context of Neural Networks

“…So far, many types of artificial neural networks have appeared, mainly including feedback networks, radial basis function neural networks, linear neural networks, self-organizing neural networks, and backpropagation neural networks [4]. Radial basis function neural network and backpropagation neural network have very high application values relative to nonlinear fitting and have good performance [5].…”

Privacy-preserving sports data fusion and prediction with smart devices in distributed environment

“…Moreover, the use of federated learning in pedestrian trajectory prediction models has been explored, offering better data privacy security and prediction performance [32][33][34][35]. The development of medical sports data privacy protection methods based on legal risk control highlights the importance of standardizing data handling practices in the medical field [36]. An edge computing-based method for big data privacy protection in martial arts training movement trajectory prediction addresses both accuracy and security concerns [37].…”

Current development and prospects of deep learning in spine image analysis: a literature review

“…Another issue is patient privacy protection. In general, leakage of patients' privacy will not only damage the reputation of patients and cause mental stress to individuals, but also may cause economic losses (147). For example, after private medical data is leaked, the patient may be distressed by the potential of others knowing or even misusing their medical information.…”

“…Patient long-term care insurance premiums may subsequently rise, and insurance companies may directly refuse to provide life insurance (148). In addition, employment discrimination may also occur (147,149). Therefore, it is necessary to protect patient privacy in the process of sharing and using patients' data (147,150,151).…”

“…In addition, employment discrimination may also occur (147,149). Therefore, it is necessary to protect patient privacy in the process of sharing and using patients' data (147,150,151).…”