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Paper Citation Record · LEDGER

A Unified Framework for Quantifying Privacy Risk in Synthetic Data

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2211.10459.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2211.10459 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T11:32:35.928481Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-15T08:09:51.165237Z

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 dc0584a3-fa39-4c79-826c-c5ac23af25e5 · inbound

Enforcing Demographic Coherence: A Harms Aware Framework for Reasoning about Private Data Release cites this paper.

Enforcing Demographic Coherence: A Harms Aware Framework for Reasoning about Private Data Release A Unified Framework for Quantifying Privacy Risk in Synthetic Data

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-09T11:32:35.928481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T11:32:35.928481Z digest=sha256:718ace7edaab752041981e3cb672101d502661bf6a46efae9299b7e6e1bac390

Observation 4d37c176-1d65-4b41-be2e-f8d53f4445ea · inbound

MIDST Challenge at SaTML 2025: Membership Inference over Diffusion-models-based Synthetic Tabular data cites this paper.

MIDST Challenge at SaTML 2025: Membership Inference over Diffusion-models-based Synthetic Tabular data A Unified Framework for Quantifying Privacy Risk in Synthetic Data

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-15T08:09:51.168293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T08:07:25.368762Z digest=sha256:1c4f012dbbceb884a948cb58d103d9d81b24d4698d413eaf68019f862989e4b8