Description: Attribute generalization is a data anonymization method that involves replacing specific values with more general ones, with the aim of protecting individuals’ privacy within a dataset. This approach allows the information to remain useful for analysis and studies while minimizing the risk of identifying individuals. For instance, instead of recording a person’s exact age, an age range like ’20-30 years’ could be used. This process not only helps preserve confidentiality but also facilitates compliance with data protection regulations, such as GDPR in Europe. Attribute generalization is based on the idea that by making data less specific, the likelihood of linking it to a particular person is reduced. This method is especially relevant in contexts where sensitive data is handled, such as in medical research or market studies, where participant privacy is crucial. In summary, attribute generalization is an essential technique in data anonymization that allows for a balance between the utility of information and the need to protect individuals’ identities.
