Paper Citation Record · LEDGER

Differentially Private Diffusion Models Generate Useful Synthetic Images

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2302.13861.

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

pith.paper-citation-record.v1
2302.13861 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:56:47.087928Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-11T01:47:47.905189Z

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 d2823d9c-dc27-4380-99e2-ae38a5652214 · inbound

CollaFuse: Collaborative Diffusion Models cites this paper.

CollaFuse: Collaborative Diffusion Models Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:35:52.201089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 103d1ab8-dc8e-42a4-b23b-15d773f70d6e · inbound

Privacy Leakage via Output Label Space and Differentially Private Continual Learning cites this paper.

Privacy Leakage via Output Label Space and Differentially Private Continual Learning Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T17:23:15.470770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bc758505-3f81-4876-a21b-66ee7e1a0cb8 · inbound

Scaling Laws for Differentially Private Language Models cites this paper.

Scaling Laws for Differentially Private Language Models Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-09T22:05:02.566936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation debbf19e-4b1b-4479-9290-ba1c8306e427 · 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 Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T19:09:03.919231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6af842c8-0e5d-4621-af22-ca0d19f67a5a · 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 Diffusion Models Generate Useful Synthetic Images

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T15:07:10.051077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ae7e6cc-f78b-4881-8626-71bfdafe5d11 · inbound

Boosting 3D Liver Shape Datasets with Diffusion Models and Implicit Neural Representations cites this paper.

Boosting 3D Liver Shape Datasets with Diffusion Models and Implicit Neural Representations Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T05:56:47.087928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d086eed-02ff-4b41-bf02-029684f548c7 · inbound

PCEvolve: Private Contrastive Evolution for Synthetic Dataset Generation via Few-Shot Private Data and Generative APIs cites this paper.

PCEvolve: Private Contrastive Evolution for Synthetic Dataset Generation via Few-Shot Private Data and Generative APIs Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T10:57:16.966605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bef36e3-93f7-4155-8bc2-01799387f42b · inbound

Private Training & Data Generation by Clustering Embeddings cites this paper.

Private Training & Data Generation by Clustering Embeddings Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 2000

Resolution: unresolved; no resolver link, observed 2026-08-15T19:27:53.726119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3feecb2-1cac-431f-973f-edae722a5dfb · inbound

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation cites this paper.

SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:16.909245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfb60799-591a-478f-8eb8-6f5e917d2c22 · inbound

Machine Learning with Privacy for Protected Attributes cites this paper.

Machine Learning with Privacy for Protected Attributes Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T18:39:11.386421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8475ac6b-768b-4585-8965-4a3c27e1a75f · inbound

Implementing Adaptations for Vision AutoRegressive Model cites this paper.

Implementing Adaptations for Vision AutoRegressive Model Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T17:12:33.396844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a80cdf79-fa5a-4a70-874f-75b6fd987053 · inbound

Improving Noise Efficiency in Privacy-preserving Dataset Distillation cites this paper.

Improving Noise Efficiency in Privacy-preserving Dataset Distillation Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T05:32:44.944068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccb58872-9a6e-4e63-a81f-b64db7d6de84 · inbound

DP-DocLDM: Differentially Private Document Image Generation using Latent Diffusion Models cites this paper.

DP-DocLDM: Differentially Private Document Image Generation using Latent Diffusion Models Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T00:51:45.214645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ce6dd73-fee1-4e96-a348-bdd92117a5a9 · inbound

On the MIA Vulnerability Gap Between Private GANs and Diffusion Models cites this paper.

On the MIA Vulnerability Gap Between Private GANs and Diffusion Models Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T16:35:03.616287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b57cb7d3-6b45-43d0-8c3d-6cc73875085e · inbound

Struct-Bench: A Benchmark for Differentially Private Structured Text Generation cites this paper.

Struct-Bench: A Benchmark for Differentially Private Structured Text Generation Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T15:57:47.573745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3581325-4cc7-4c23-b8e6-786f465d26ad · inbound

DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis cites this paper.

DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T14:33:30.899539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 55e9ead4-2186-4a12-8a0d-f50bf481c090 · inbound

PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality cites this paper.

PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 87

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T04:09:34.569355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d0a5a82c-7fae-4054-b5ba-bb589f51e48e · inbound

WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning cites this paper.

WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 23

Resolution: metadata mismatch; local_arxiv, observed 2026-07-11T01:47:47.926104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4850f5a0-48cc-48fb-a5ed-f690ec5158f5 · inbound

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

Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation Differentially Private Diffusion Models Generate Useful Synthetic Images

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T16:26:23.721901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.