Pith. sign in

Paper Citation Record · LEDGER

The 2020 Census Disclosure Avoidance System TopDown Algorithm

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2204.08986.

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

pith.paper-citation-record.v1
2204.08986 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:55:17.865828Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T21:00:09.719594Z

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 14856370-c5b9-4918-b5aa-941888f80453 · inbound

Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysis cites this paper.

Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysis The 2020 Census Disclosure Avoidance System TopDown Algorithm

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-11T12:27:40.274622Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T12:27:40.274622Z digest=sha256:34156e8b127de8fc00f54f574745246ffb964a6eb3241570211227290331589a

Observation fde4fb44-5b9d-41b2-8c35-1cda1fd87bdd · inbound

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data cites this paper.

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data The 2020 Census Disclosure Avoidance System TopDown Algorithm

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-16T11:55:17.865828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T11:55:17.865828Z digest=sha256:8ec8e3cdcba749ac813c2fdc66ed33fb499aba9ea2179b92c09674f965db8fc5

Observation 39f44211-5f12-4142-bdd4-c1d4cdb1c731 · inbound

Bayesian Nonparametric Privacy-Preserving Synthetic Data Generation: I. Discrete Data cites this paper.

Bayesian Nonparametric Privacy-Preserving Synthetic Data Generation: I. Discrete Data The 2020 Census Disclosure Avoidance System TopDown Algorithm

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-04T21:00:09.721403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-25T19:10:16.618050Z digest=sha256:315ad9fb963fd121ea17561d30131331a1f62d823df7b0ef1434c42b70c3bdbd