Análise do equilíbrio entre privacidade e utilidade no acesso a dados

Abstract

This article analyzes measures for privacy protection by means of non-perturbative and perturbative techniques, aiming equilibrium between privacy and utility of data. Then, suppression and generalization were used for anonymization of medical consultation data and, based on Mivule (2015) the addition of noises was performed to generate private modified data. Subsequently, the utility of those data was verified by means of cluster analysis. The results showed the generalization preserves the accuracy of the data and, when combined with the addition of noises, can be a viable alternative to disclose data, minimizing risks to privacy and with an acceptable level of utility.