Errors of Identifiers in Anonymous Databases: Impact on Data Quality

Published: 01 Jan 2022, Last Modified: 22 Aug 2024SOCO 2022EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Data quality is essential for a correct understanding of the concepts they represent. Data mining is especially relevant when data with inferior quality is used in algorithms that depend on correct data to create accurate models and predictions. In this work, we introduce the issue of errors of identifiers in an anonymous database. The work proposes a quality evaluation approach that considers individual attributes and a contextual analysis that allows additional quality evaluations. The proposed quality analysis model is a robust means of minimizing anonymization costs.
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