Data anonymization is a procedure by which the most attributes within a data record are replaced by one or more artificial identifiers, or pseudonyms.
There can be a single pseudonym for a collection of replaced fields or a pseudonym per replaced field. The purpose is to render the data record less identifying and therefore lower End-User or patient objections to its use.
Data anonymization is the process of either encrypting or removing personally Identifiable Information from data sets, so that the Personal Entity whom can remain anonymous.
The Privacy Technology Focus Group defines Data anonymization as "technology that converts clear text data into a non-person entity readable and irreversible form, hashing and encryption techniques in which the decryption key has been discarded."
Data anonymization enables the Data In Transit across a boundary, such as between two departments within an agency or between two agencies, while reducing the risk of unintended Disclosure, and in certain environments in a manner that enables evaluation and analytics post-anonymization.
Following Data anonymization on Protected Health Information, the data is no longer Protected Health Information and is referred to a Health Dataset
De-anonymization is the reverse process in which anonymous data is cross-referenced with other data sources to re-identify the anonymous data source.
Generalization and perturbation are the two popular anonymization approaches for relational data.