Description: Logical data anonymization is a method that focuses on the logical representation of data for anonymization. This approach allows for the transformation of sensitive data into a form that preserves its utility for analysis and studies while eliminating the possibility of identifying specific individuals. Unlike direct anonymization, which may involve the removal of identifying data, logical anonymization seeks to maintain the structure and relationships of the data, using techniques such as generalization and perturbation. This means that, although the data has been modified to protect individuals’ identities, it can still be used to obtain valuable information and perform statistical analyses. Logical data anonymization is particularly relevant in contexts where compliance with data protection regulations, such as GDPR in Europe, is required, which mandates that personal data be handled in a way that ensures individuals’ privacy. This method has become increasingly important in the age of Big Data, where the collection and analysis of large volumes of data are common, and privacy protection has become a central concern.
