Laboratory of Embedded and Distribution Systems, University of Vale do Itajaí, Rua Uruguai 458, C.P. 360, Itajaí 88302-901, Brazil.
Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, Plaza de los Caídos s/n, 37008 Salamanca, Spain.
Sensors (Basel). 2020 Oct 23;20(21):6030. doi: 10.3390/s20216030.
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.
人群感染诊断数据对于公共和私营领域的不同应用具有战略意义。其中,与症状和人员流动相关的数据尤为突出,主要考虑到高传染性疾病。这些数据具有敏感性,需要数据隐私倡议来支持其大规模使用。人群监测策略的目的是进行社会追踪,支持传染病监测,以应对 COVID-19 的挑战。在使用物联网设备监测医院流程的环境中,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主题相关的工作。为此,本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需的要求。根据对比较变量进行的测试和比较,应用程序获得了有助于应用场景的结果。从这个意义上说,我们对应用程序进行了建模和实现。最后,开发了一个移动应用程序来分析与 COVID-19 相关的隐私和安全约束。