Description: Graphical anonymization is an innovative approach that uses visual methods to represent and conceal sensitive data, thus ensuring information privacy. This process involves transforming original data into graphical representations that preserve patterns and trends while removing identifiable details. Through techniques such as aggregation, distortion, and generalization, it creates visualizations that allow data analysis without compromising the identity of the individuals involved. Graphical anonymization is especially relevant in a world where personal data protection is crucial, as it enables organizations to share valuable information without the risk of exposure. Additionally, it facilitates the understanding of large volumes of data by presenting information in an intuitive and accessible manner, which is essential for informed decision-making. This approach has become increasingly popular in various fields, including research, business analytics, and data intelligence, where the need to balance data utility with privacy is paramount.
