Paper Citation Record · LEDGER

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training

As of 13 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2412.09842.

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

pith.paper-citation-record.v1
2412.09842 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:47:59.197683Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact1
verified fuzzy20
unresolved8
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 72b01606-c95c-4e98-b0cd-802cf03c7b4f · outbound

This paper cites Differential privacy,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Differential privacy,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.763293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 77fb6d08-f159-4f42-b380-4ef920d48e19 · outbound

This paper cites In-distribution public data synthesis with diffusion models for differentially private image classification,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training In-distribution public data synthesis with diffusion models for differentially private image classification,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.745004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 99d6cf8b-b34d-4cb9-a95a-459f9d8face1 · outbound

This paper cites Unlocking High-Accuracy Differentially Private Image Classification through Scale.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Unlocking High-Accuracy Differentially Private Image Classification through Scale

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:59.075516Z

Source-reported events for the cited work

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Observation 87e62ce6-a476-4359-807e-086610937d1a · outbound

This paper cites Position: Consid- erations for differentially private learning with large-scale public pretraining,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Position: Consid- erations for differentially private learning with large-scale public pretraining,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.723812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6c0721d7-99c0-4ba2-b7bd-063dfc32ec09 · outbound

This paper cites Dif- ferentially Private Diffusion Models,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Dif- ferentially Private Diffusion Models,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.701667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a5c7f0ae-7037-4c4b-8fcc-20bb905e8c1c · outbound

This paper cites Learning to see by looking at noise,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Learning to see by looking at noise,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.686269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ccea4b17-1b05-4b02-957a-3ef868f86ec2 · outbound

This paper cites Differ- entially private image classification by learning priors from random processes,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Differ- entially private image classification by learning priors from random processes,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.670961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 840ab18a-2696-4241-bfa9-f3460a51885a · outbound

This paper cites ViP: A Differentially Private Foundation Model for Computer Vision.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training ViP: A Differentially Private Foundation Model for Computer Vision

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:59.099339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a044a4e4-55ae-4f92-aa82-42b81c30c069 · outbound

This paper cites Perception prioritized training of diffusion models,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Perception prioritized training of diffusion models,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.652528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 902135dd-0eeb-45f5-817c-5ad1ea4ba9d7 · outbound

This paper cites Fair sam- pling in diffusion models through switching mechanism,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Fair sam- pling in diffusion models through switching mechanism,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.621102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 657fdd14-bdd5-44c5-9f1a-d6576c736203 · outbound

This paper cites Denoising diffusion probabilistic models,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Denoising diffusion probabilistic models,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:59.112687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a967e0f-3ffb-4892-aa1a-919ba59ea9e1 · outbound

This paper cites Varia- tional diffusion models,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Varia- tional diffusion models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.586396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8902e09f-de7c-462e-a359-bec38c378a8a · outbound

This paper cites Elucidating the design space of diffusion-based generative mod- els,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Elucidating the design space of diffusion-based generative mod- els,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.566719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e161fe6b-2319-460b-aa66-da6f3ed43504 · outbound

This paper cites Efficient diffusion training via min-snr weighting strategy,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Efficient diffusion training via min-snr weighting strategy,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.546234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 56c106d0-9196-4b62-8ac5-2dc89410c5b2 · outbound

This paper cites Deep learning with differential privacy,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Deep learning with differential privacy,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:59.131572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c569bf3e-4431-403c-b0c7-7a3efba7b15a · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:59.136060Z

Source-reported events for the cited work

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Observation d98bb825-a0b6-44a9-8e17-73162b587922 · outbound

This paper cites Classification accuracy score for conditional generative models,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Classification accuracy score for conditional generative models,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.493517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2f83ef7f-f34c-47c7-b57e-4240606681fd · outbound

This paper cites Denoising Diffusion Implicit Models.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Denoising Diffusion Implicit Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:59.144837Z

Source-reported events for the cited work

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Observation 70466643-d72f-4f5c-bcdb-08d80930971e · outbound

This paper cites Opacus: User-Friendly Differential Privacy Library in PyTorch.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:59.149451Z

Source-reported events for the cited work

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Observation 9a5388be-f525-43da-8c1d-a08a5c48b764 · outbound

This paper cites PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-11T16:47:59.262388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8c20be3a-94de-4cd6-b16a-ba6741e636c1 · outbound

This paper cites Private GANs, Revisited.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Private GANs, Revisited

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T16:47:59.159600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cd10b9d-da36-46ea-9662-486a8296a24a · outbound

This paper cites Hermite polynomial features for private data generation,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Hermite polynomial features for private data generation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.477741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 75b45e9e-12d8-4d17-b009-c201840daddb · outbound

This paper cites Don’t generate me: Training differentially private generative models with sinkhorn divergence,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Don’t generate me: Training differentially private generative models with sinkhorn divergence,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.460869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 83de240d-4b63-41e0-b61b-51fffddc1ab8 · outbound

This paper cites Scalable differentially private data generation via private aggregation of teacher ensembles,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Scalable differentially private data generation via private aggregation of teacher ensembles,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.435226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4582e476-6468-4d15-9695-3a304a9b9dae · outbound

This paper cites Dp- cgan: Differentially private synthetic data and label gen- eration,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Dp- cgan: Differentially private synthetic data and label gen- eration,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.414942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 82c06007-5c3a-478d-98c5-207e9f7a2dfa · outbound

This paper cites Datalens: Scalable privacy preserving training via gradient compression and aggregation,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Datalens: Scalable privacy preserving training via gradient compression and aggregation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.396367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b6ad9f0c-e958-481c-9034-73ba30e67661 · outbound

This paper cites Dp-merf: Differentially private mean embeddings with randomfea- tures for practical privacy-preserving data generation,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Dp-merf: Differentially private mean embeddings with randomfea- tures for practical privacy-preserving data generation,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.380746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 469f1977-98aa-4ddd-a31a-fc1bbb11bc0f · outbound

This paper cites Dpgen: Differentially private generative energy- guided network for natural image synthesis,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Dpgen: Differentially private generative energy- guided network for natural image synthesis,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.363962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b41ba32a-14b8-48de-b60f-3e67fbdae2ba · outbound

This paper cites Dead leaves models: from space tessellation to random functions,.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training Dead leaves models: from space tessellation to random functions,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T16:47:59.347218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.