Exame de Qualificação: A metamodel for predicting the effects of Iinfectious diseases on healthcare systems with encrypted data

Orientadores:
Fábio Borges de Oliveira - Laboratório Nacional de Computação Científica - LNCC

Banca Examinadora:
Renato Portugal - Laboratório Nacional de Computação Científica - LNCC (presidente)
Bruno Richard Schulze - Laboratório Nacional de Computação Científica - LNCC
Rafael Timóteo de Sousa Jr - UNB

Suplentes:
Jack Baczynski - Laboratório Nacional de Computação Científica - LNCC

Resumo:

Protecting personal data in our information-based era is a challenge. Security and privacy are important in all areas of our life, especially with regards to health. Infectious diseases generate social stigmas in patients, and hospitals have the challenge of dealing with the current pandemic caused by COVID-19 and ensure privacy in healthcare systems. To mitigate the inherent risks of unauthorized access to data, system administrators can use hardware and software for ensuring data management mechanisms. However, we still see massive data leaks on the internet because deceptive administrators and cyber-attacks on computer vulnerabilities can give an attacker administrative access and, therefore, full access to servers and their data. Considering the constant cyber-attacks on the web and the imminent risk of privacy leakage, the goal of this work is to propose a metamodel that adapts a computational model to securely process sensitive data with cryptography, specifically, homomorphic encryption based on lattice. The computational model predicts the effects of infectious diseases on healthcare systems, and its authors present a numerical simulation based on data collected during the COVID-19 pandemic. We present the metamodel results of a numerical simulation based on the same data, using homomorphic operations, which its both accurately and precisely in comparison with the original model results but at the cost of a significant increase in time. Nevertheless, the metamodel is still computationally workable and significantly fast enough for users. Therefore, we ensure security and privacy for predicting the effects of infectious diseases on healthcare systems independent of deceptive administrators and reducing the computer vulnerabilities to mathematical problems.