Synthetic Data — Anonymisation Groundhog Day¶
Stadler, T., Oprisanu, B., & Troncoso, C. (2022). Synthetic Data — Anonymisation Groundhog Day. 31st USENIX Security Symposium, 1451-1468.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Synthetic Data Testbed
- The honest failure mode is a fidelity gap in the wrong place — the synthetic data misses a real correlation, so a model that looks strong in the testbed fails in production — or the mirror-image failure, where fidelity is pushed so high the generator memorizes real people and the "anonymous" data leaks them.
This sourceShows that synthetic data may unpredictably suppress useful statistical signals while still exposing individuals to linkage and attribute-inference attacks.
- The honest failure mode is a fidelity gap in the wrong place — the synthetic data misses a real correlation, so a model that looks strong in the testbed fails in production — or the mirror-image failure, where fidelity is pushed so high the generator memorizes real people and the "anonymous" data leaks them.
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:6cb2cb1461e3 · see in the full table