Perform privacy-leakage and re-identification testing | AIGP
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Perform privacy-leakage and re-identification testing: Which control is most direct?

AIGP Understanding How to Govern AI Development Hard

Synthetic origin does not establish anonymity; privacy leakage requires direct testing.

The question

A public-benefit eligibility model uses synthetic records. Accuracy, subgroup testing, provenance, lawful processing under GDPR Articles 5 and 6, access controls, and documentation are complete. No analysis has examined whether generated records preserve identifiable patterns or enable re-identification. Which control is most direct?

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  1. Compare aggregate eligibility accuracy and error rates across beneficiary groups.
    Aggregate subgroup metrics address performance differences, not disclosure, memorization, or re-identification risk in generated records.
  2. Perform privacy-leakage and re-identification testing on outputs. ✓
    These tests directly examine whether generated records retain sensitive patterns or can be linked back to individuals.
  3. Increase synthetic-data coverage.
    More synthetic records may improve coverage but do not show that outputs avoid memorization, linkage, or re-identification.
  4. Document the generation method and source-data limitations.
    Documentation supports provenance and accountability but does not test whether generated records leak information or remain linkable.
The trap
Ask what evidence demonstrates non-disclosure; generation alone is not that evidence.

How to remember it

Synthetic origin does not establish anonymity; privacy leakage requires direct testing.

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