Description: Partial anonymization is a data protection method that involves the removal or alteration of some personal identifiers in a dataset, while other identifiers remain intact. This approach allows data to be used for analysis and studies without fully compromising the identity of the individuals involved. Unlike total anonymization, where all identifying data is removed, partial anonymization seeks a balance between data utility and privacy. This method is particularly relevant in contexts where data is required for research, product development, or market analysis, but where personal information protection must also be considered. Partial anonymization can include techniques such as pseudonymization, where direct identifiers are replaced with pseudonyms, or generalization, where data is grouped into broader categories. This approach allows organizations to maintain the ability to conduct meaningful analyses without exposing individuals’ identities, which is crucial in an environment where data privacy is increasingly valued and regulated.
