Anonymization
Irreversibly stripping data of any link back to an identifiable individual, such that the data falls outside the scope of most privacy law (GDPR Recital 26). Distinct from pseudonymization, which retains a re-identification path and therefore does not remove the data from privacy law's scope.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in AI Data Governance: The Data Chair.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
The process of removing or altering identifying information from data so it can no longer be directly linked to a specific individual. A meaningful but imperfect mitigation, since re-identification research has shown that anonymized location and movement data can often still be linked back to individuals through pattern matching.
Irreversibly stripping data of any link back to an identifiable individual, such that the data falls outside the scope of most privacy law (GDPR Recital 26). Distinct from pseudonymization, which retains a re-identification path and therefore does not remove the data from privacy law's scope.
The removal or transformation of identifiers so data no longer obviously identifies individuals. Recorded in provenance as a mitigation, not a clearance: it can be reversible through re-identification and does not retroactively supply a lawful basis for the original collection.
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- The deletion decision: the terabytes your organization should destroy this quarter, defended · The Money of Data, AI Data Governance: The Data Chair
- Auditing your own data: provenance, gaps, and quiet poison · Data Reality, AI Governance: Applied Mastery
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.