Description: Generalized suppression is a data anonymization technique that involves removing or modifying specific data points within a dataset to prevent the identification of individuals. This approach is fundamental in the field of data protection, as it allows information to be used for analysis and studies without compromising individuals’ privacy. Suppression can be applied to sensitive data, such as names, addresses, or identification numbers, and is conducted in such a way that the remaining data retains its utility for research or statistical analysis. This technique differs from other forms of anonymization, such as perturbation or masking, as it focuses on the complete removal of identifying data rather than its modification. Generalized suppression is particularly relevant in contexts where privacy is critical, such as in the handling of medical, financial, or behavioral data, where identifying an individual could have negative consequences. By implementing this technique, organizations can comply with data protection regulations, such as the GDPR in Europe and similar laws worldwide, which require strict measures to safeguard personal information.
