The coronavirus disease 2019 (COVID-19) (2019-nCov), which was first detected in Wuhan/China in December 2019 and spread to the whole world in a short time, was explained as a new coronavirus by the World Health Organization on February 11, 2020. Countries are developing various strategies against the spread of epidemic threat. The main ones are to develop web-based or mobile applications to reduce the spread and economic damage of the epidemic by making use of COVID-19 datasets. It is seen that the existing applications developed within the framework of these expectations contain absolute location information (direct), relative location information (indirect), and characteristic data defining people. Even if these data mean a lot to the world's struggle with COVID-19, it is necessary to foresee the risks that may occur after the epidemic when the relations of the information are considered. In order to measure the privacy risk of this kind of applications containing personal data, privacy metrics have been defined in the literature. In this chapter, we give a perspective about the sharing and privacy of medical data within the scope of COVID-19. Within this context, privacy models, metrics, and approaches for selecting the appropriate model are described, in particular for COVID-19 applications, and we also propose a new metric with the entropy approach to metrics defined in the literature and effective in determining the privacy score.
Data sharing and privacy issues arising with COVID-19 data and applications