Paper Citation Record · LEDGER

HDMM: Optimizing error of high-dimensional statistical queries under differential privacy

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2106.12118.

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

pith.paper-citation-record.v1
2106.12118 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:28:45.160325Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T15:24:49.943892Z

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 b8620d90-046b-473d-ad9e-60dfad0e6c85 · inbound

ResidualPlanner+: a scalable matrix mechanism for marginals and beyond cites this paper.

ResidualPlanner+: a scalable matrix mechanism for marginals and beyond HDMM: Optimizing error of high-dimensional statistical queries under differential privacy

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-24T08:14:10.135002Z

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.

Observation 953c0861-b494-485c-a979-024d53e228a4 · inbound

Correlated Noise Mechanisms for Differentially Private Learning cites this paper.

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T05:28:45.160325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbc95c41-a018-41f6-873f-7171a298f2fb · inbound

Aim High, Stay Private: Differentially Private Synthetic Data Enables Public Release of Behavioral Health Information with High Utility cites this paper.

Aim High, Stay Private: Differentially Private Synthetic Data Enables Public Release of Behavioral Health Information with High Utility HDMM: Optimizing error of high-dimensional statistical queries under differential privacy

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-19T07:32:08.704069Z

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.

Observation 0f44fdca-e2b5-4c05-9ae0-e500df83e71a · inbound

Answering Counting Queries with Differential Privacy on a Quantum Computer cites this paper.

Answering Counting Queries with Differential Privacy on a Quantum Computer HDMM: Optimizing error of high-dimensional statistical queries under differential privacy

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:56:01.309778Z

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.

Observation 866efb89-d411-4508-b9fb-3b453760d1f5 · inbound

Private Adaptive Covariance Estimation via Gaussian Graphical Models cites this paper.

Private Adaptive Covariance Estimation via Gaussian Graphical Models HDMM: Optimizing error of high-dimensional statistical queries under differential privacy

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:24:49.945578Z

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.

Observation ed2bce12-71ea-48c8-a657-bb24c42dfcde · inbound

Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation cites this paper.

Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation HDMM: Optimizing error of high-dimensional statistical queries under differential privacy

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-01T16:26:23.806287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.