Data anonymization is the use of one or more techniques designed to make it impossible -- or at least more difficult -- to identify a particular individual from stored data related to them.
The purpose of data anonymization is to protect the privacy of the individual and to make it legal for governments and businesses to share their data without getting permission. Data anonymization methods include encryption, hashing, generalization, pseudonymization and perturbation.
De-anonymization is a reverse engineering process used to detect the source data. The most common method of de-anonymization is cross-referencing data from multiple sources, some of which are typically in the public record and contain the personally identifying information (PII) that is being sought.
See a webcast on anonymizating health data: