Description: Numerical anonymization refers to techniques used to anonymize numerical data, ensuring that individual values cannot be traced back to specific individuals. This process is fundamental in the context of data protection, as it allows the use of valuable information without compromising individuals’ privacy. Numerical anonymization involves modifying numerical data in such a way that any possibility of direct or indirect identification of the individuals to whom they belong is eliminated. This can be achieved through various techniques, such as data perturbation, generalization, or aggregation. The relevance of numerical anonymization has grown in the digital age, where the collection and analysis of large volumes of data are common. Organizations must comply with privacy regulations, such as the GDPR in Europe, which requires that personal data be handled in a way that protects individuals’ identities. By applying numerical anonymization, organizations can continue to gain valuable insights from their data without risking sensitive information about their customers. In summary, numerical anonymization is an essential tool for balancing the need for data analysis with the obligation to protect individual privacy.
