Open AccessPrivacy-preserving of patients with Differential Privacy: an experimental evaluation in COVID-19 dataset
Author(s) -
Manuel Edvar Bento Filho,
Eduardo Rodrigues Duarte Neto,
Javam C. Machado
Publication year - 2021
Publication title -
journal of information and data management
Language(s) - English
Resource type - Journals
ISSN - 2178-7107
DOI - 10.5753/jidm.2021.1947
Subject(s) - differential privacy , internet privacy , government (linguistics) , information privacy , pandemic , covid-19 , computer science , private information retrieval , privacy by design , health care , computer security , privacy policy , privacy software , work (physics) , data mining , medicine , political science , law , engineering , mechanical engineering , linguistics , philosophy , disease , pathology , infectious disease (medical specialty)
The pandemic of the new coronavirus (COVID-19) has brought new challenges to health systems in almost every corner of the world, many of them overburdened. The data analysis has given support in the fight against the coronavirus. Through this analysis, government authorities, together with health care providers, adopted effective strategies. Yet, those strategies can not be careless of privacy concerns. The individuals’ privacy is a right of each citizen. Privacy techniques guarantee the analysis of health data without exposing individuals’ private information. However, a balance between data privacy and utility is essential for a good analysis of the data. This work will demonstrate that it is possible to guarantee the privacy of infected patients and maintain the utility of the data, allowing a sound analysis on them, from the visualization of the application of differentially private mechanisms on queries in the data of patients tested in the State of Ceará - Brazil.