Description: Multidimensional anonymization is an approach that considers multiple dimensions of data to ensure comprehensive anonymization. This method focuses on protecting individuals’ privacy by removing or modifying information that could be used to identify a specific person. Unlike simpler techniques that may focus on a single dimension, such as removing names or direct identifiers, multidimensional anonymization addresses the complexity of data as a whole. This includes considering various variables and their interrelationships, allowing for greater robustness in protecting sensitive information. Key features of this approach include preserving data utility, reducing the risk of re-identification, and adapting to different usage contexts. Multidimensional anonymization is especially relevant in a world where data collection is ubiquitous and privacy concerns are increasingly critical. By implementing this approach, organizations can comply with data protection regulations while still leveraging the value of data for analysis and decision-making without compromising the privacy of the individuals involved.
