Description: Granular anonymization refers to anonymization techniques that operate at a very specific level of detail of the data. This approach allows for the modification or removal of sensitive information in a way that preserves the utility of the data for analysis and studies while protecting the identity of the individuals involved. Unlike more general methods that may affect large data sets, granular anonymization focuses on specific attributes, such as names, addresses, or identification numbers, allowing other data to remain intact. This is especially relevant in contexts where a balance between privacy and data utility is required, such as in medical research, market studies, or behavior analysis. Techniques may include data perturbation, generalization, or the use of pseudonyms, and are fundamental to comply with data protection regulations, such as GDPR in Europe. Granular anonymization not only helps mitigate the risks of exposing personal data but also allows organizations to continue gaining valuable insights without compromising individual privacy.
