Privacy-preserving of patients with Differential Privacy: an experimental evaluation in COVID-19 dataset


  • Manuel E. B. Filho Universidade Federal do Ceará
  • Eduardo R. Duarte Neto Universidade Federal do Ceará
  • Javam C. Machado Universidade Federal do Ceará



COVID-19, differentially private publication, data analysis


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.


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How to Cite

B. Filho, M. E., Duarte Neto, E. R., & C. Machado, J. (2021). Privacy-preserving of patients with Differential Privacy: an experimental evaluation in COVID-19 dataset. Journal of Information and Data Management, 12(5).



SBBD 2020 Short papers - Extended Papers