Description: Attribute suppression is a technique used in data anonymization that involves removing certain attributes or characteristics from a dataset in order to protect the identity of the individuals to whom that data belongs. This technique is fundamental in the context of privacy and data protection, as it allows information to be used for analysis and studies without compromising the confidentiality of individuals. Suppression can be applied to directly identifying attributes, such as names and addresses, as well as to attributes that, while not identifying on their own, could allow for the re-identification of an individual when combined with other data. Attribute suppression is especially relevant in various sectors, including healthcare, where sensitive data is handled, and in the business realm, where market analysis is sought without exposing personal information of customers. This technique can be implemented in various ways, from the complete removal of certain fields to the generalization of data, where specific values are replaced by broader categories. In summary, attribute suppression is a key tool in data management that seeks to balance the utility of information with the need to protect individual privacy.
