Anonymization
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.
Defined in 3 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in Engineering Judgment and Professional Formation.
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 module of every program is free with a free account.
- The deletion decision: the terabytes your organization should destroy this quarter, defended · The Money of Data, Certified AI Data Governance Professional (CADGP)
- Auditing your own data: provenance, gaps, and quiet poison · Data Reality, Certified AI Governance Professional (CAIGP)
- Privacy when a robot's sensors record real spaces · Responsible Physical AI in Practice, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Anonymization starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Builds minimization, purpose limitation, de-identification, consent and retention into an AI system before launch, with tests that prove it.
One lesson on this page teach it as a primary outcome.
Profiles data, sets rules and thresholds, monitors, assigns issues to owners, and knows when data is not fit to train or run a model.
One lesson on this page teach it as a primary outcome.
Traces where data came from, what transformed it, who owns each hop and where it flows downstream, so a number can be defended.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- AI Privacy Engineerneeds it at depth · 6 open postings this week
- Chief Data Officer, data leadership editionneeds it at depth
- Chief Privacy Officer, AI privacy editionneeds it at depth
- Data Governance Leadneeds it at depth · 2 open postings this week
- Privacy Analystneeds working knowledge
- AI Model Validatorneeds working knowledge
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: ISO/IEC 27701; GDPR Article 25; EU AI Act Article 10; ISO 8000.
One interview question about it
How do you build privacy into an AI product from the design stage rather than checking it at the end?
Asked of candidates for privacy by design and privacy engineering. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Data analysis, stewardship and business intelligence carries it as work you can show: Quality rules and profiling results are your artifacts.
- A data science, statistics or analytics degree carries it as a claim to back up: Data quality rules are familiar; monitoring them in production is new.
- Privacy and data protection carries it as work you can show: Data maps and records of processing are lineage by another name.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.