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Abstract：
Data on diagnosis of infection in the general population are strategic for different applications in the public and private spheres. Among them, the data related to symptoms and people displacement stand out, mainly considering highly contagious diseases. This data is sensitive and requires data privacy initiatives to enable its large-scale use. The search for population-monitoring strategies aims at social tracking, supporting the surveillance of contagions to respond to the confrontation with COVID-19. There are several data privacy issues in environments where IoT devices are used for monitoring hospital processes. In this research, we compare works related to the subject of privacy in the health area. To this end, this research proposes a taxonomy to support the requirements necessary to control patient data privacy in a hospital environment. According to the tests and comparisons made between the variables compared, the application obtained results that contribute to the scenarios applied. In this sense, we modeled and implemented an application. By the end, a mobile application was developed to analyze the privacy and security constraints with COVID-19.
摘要：
一般人群感染诊断数据对于公共和私人领域的不同应用具有战略意义。其中,与症状和流离失所者相关的数据脱颖而出，主要考虑高度传染性疾病。这些数据是敏感的，需要数据隐私措施才能大规模使用。寻找人口监测策略的目的是社会跟踪，支持对传染病的监测，以应对与COVID-19的对抗。在使用物联网设备监控医院流程的环境中，存在一些数据隐私问题。在这项研究中,我们比较了与健康领域隐私相关的作品。为此,这项研究提出了一个分类法，以支持在医院环境中控制患者数据隐私所必需的要求。根据比较变量之间的测试和比较，应用程序获得的结果有助于应用的场景。在这个意义上,我们建模并实现了一个应用程序。到最后,开发了一个移动应用程序来分析COVID-19的隐私和安全限制。
