Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1911.12704.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T05:02:17.477543Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T00:49:48.673155Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation c07611a7-6e23-4158-8140-49bfd6d1e244 · inbound
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model cites this paper.
Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Model Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:09:03.850176Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e4f18cf5-4089-44e3-90de-16916ace2e7c · inbound
Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data cites this paper.
Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:02:17.477543Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f61516fd-4a11-4186-8b04-0f8d2d3e81a9 · inbound
Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models cites this paper.
Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T00:49:48.674368Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.