AI engineering glossary · AI ethics law product
What is k-anonymity?
k-anonymity: A privacy property of a released table: every record is indistinguishable from at least k-1 others on the quasi-identifiers, the attributes like age band and pin code that could be linked to outside data. Larger k makes singling out harder, though it can still leak sensitive values.
k-anonymity: an example
A table is 5-anonymous if every combination of age band, gender and pin-code prefix appears for at least five people.
Related terms
More AI ethics law product terms
- Label bias
- Lawful basis
- LIME
- Local explanation
- Machine unlearning
- Model card
- North Star metric
- OECD AI Principles
Where you learn it
k-anonymity is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.