Paper Citation Record · LEDGER

Differentially Private Synthetic Data via Foundation Model APIs 1: Images

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2305.15560.

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

pith.paper-citation-record.v1
2305.15560 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:07:10.068657Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T17:33:45.370024Z

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 2fb47446-9c3d-4c72-ab0e-3bca22d3cbf7 · inbound

Textbooks Are All You Need cites this paper.

Textbooks Are All You Need Differentially Private Synthetic Data via Foundation Model APIs 1: Images

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-13T04:44:03.233840Z

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 becaf1e2-f865-4600-9f7b-e76ae8b70f2c · inbound

Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data? cites this paper.

Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data? Differentially Private Synthetic Data via Foundation Model APIs 1: Images

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T15:07:10.068657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b14b69bc-eb2d-4e26-9d9b-e3513774e6d1 · inbound

Differentially Private Synthetic Data Release for Topics API Outputs cites this paper.

Differentially Private Synthetic Data Release for Topics API Outputs Differentially Private Synthetic Data via Foundation Model APIs 1: Images

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T21:38:47.767141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64ecbd54-3185-484b-baf6-814669e8733a · inbound

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy cites this paper.

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy Differentially Private Synthetic Data via Foundation Model APIs 1: Images

Reference 145

Resolution: unresolved; no resolver link, observed 2026-08-03T18:52:59.120575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1699997-69e6-4d71-91e6-c5b9ad987603 · inbound

Minimax optimal differentially private synthetic data for smooth queries cites this paper.

Minimax optimal differentially private synthetic data for smooth queries Differentially Private Synthetic Data via Foundation Model APIs 1: Images

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T05:48:10.499227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bacacfe-7643-4617-80c1-96c59b125717 · inbound

DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models cites this paper.

DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models Differentially Private Synthetic Data via Foundation Model APIs 1: Images

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:38:19.400206Z

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 36164c9f-2d17-4df9-a05e-94a0f90fd65f · inbound

Landseer: Exploring the Machine Learning Defense Landscape cites this paper.

Landseer: Exploring the Machine Learning Defense Landscape Differentially Private Synthetic Data via Foundation Model APIs 1: Images

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-06-29T17:33:45.371943Z

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 340961fc-3fd2-4e5d-b866-3feef06a813f · inbound

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data cites this paper.

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data Differentially Private Synthetic Data via Foundation Model APIs 1: Images

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T11:15:50.220869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.