Privacy-preservation and the Use of Data for Research: A COVID-19 Use Case in Randomly Generated Healthcare Records

Abstract

The provision of clinical data for research purposes has become central to monitoring and understanding the COVID-19 outbreak. In such a pandemic scenario, obtaining new research results is an imperative and urgent requirement. However, nowadays, personal data are protected by different legal regulations, to which all these data must comply, especially those related to the health of individuals. Then, a tough challenge arises in the academic sphere: how to provide a large amount of detailed clinical data for research and, simultaneously, guarantee the privacy of the individuals involved? Thus, this article discusses how the biomedical community may face this challenge and it presents the main ongoing initiatives and available emergent technologies that are useful to meet such urgent demand. Moreover, it also shows, through a use case, how it is possible to deal with this challenge, presenting the applicability of privacy-preserving techniques over a randomly generated typical dataset of COVID-19 health records.