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Paper Citation Record · LEDGER

End-to-End Differential Privacy in Training Deep Neural Network Classifiers

As of 10 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2607.19580.

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

pith.paper-citation-record.v1
2607.19580 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T12:29:21.401720Z

measured 12 of 12 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 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

12 of 12 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7530cdb0-fb63-4a36-9705-d3b11b0eebaf · outbound

This paper cites Abadi, A.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers Abadi, A

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:19.802960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:19.802960Z digest=sha256:6dfd536625bdbb0da063d5c0b5d9bc5e9dad02f32c183eeb7962528a905d2408

Observation 55043cec-a913-40ce-b816-0754e15413a6 · outbound

This paper cites DP-Image: Differential Privacy for Image Data in Feature Space.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers DP-Image: Differential Privacy for Image Data in Feature Space

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:21.078216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:21.078216Z digest=sha256:4da145ea7c86894ade163f2f11b2e4117172620662c30903a71555bcfc9994cd

Observation 537020bb-b1d3-4682-951b-b6c35be7eddb · outbound

This paper cites Wide Residual Networks.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers Wide Residual Networks

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:21.401720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:21.401720Z digest=sha256:51460f4cd2082d4c8b21da532645352014226bf75dd2eaf5d5921108cec04bff

Observation 06c34cd4-ff0f-4392-bdc8-7c11e89520e2 · outbound

This paper cites an unresolved cited work.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers Unresolved cited work

Reference 2008

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:20.748219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:20.748219Z digest=sha256:173d181be6ead9293780b738acf5a60204d7058b821715150c3b55f389623d3b

Observation 6f108615-6fa8-463c-a1d4-a74d58e0a8e2 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers Adam: A Method for Stochastic Optimization

Reference 2014

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:20.317621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:20.317621Z digest=sha256:7cc96cdbec7552bbc7e6daf3f1966f48cf45293a6d874df94dab7265e11a4e50

Observation 6e7e1fd0-2441-46fd-951f-fa1fab33170b · outbound

This paper cites Ganju, Q.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers Ganju, Q

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:20.063452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:20.063452Z digest=sha256:4b7dd430832456ca1ecb507f58d701bc7bee0ef4103a2b6ba7d8b073229cda72

Observation cfdd736d-0126-48a6-ad58-6b96ff876683 · outbound

This paper cites Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers Bridging the Gap: Differentially Private Equivariant Deep Learning for Medical Image Analysis

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:20.190674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:20.190674Z digest=sha256:9d30477bf41f7c278e521e539d31649a6902b08f3c155f42373e65e3d5d136cd

Observation a1e9ad68-0d49-4442-806c-1dcf369b9a1a · outbound

This paper cites R\'enyi Differential Privacy of the Sampled Gaussian Mechanism.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:20.455712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:20.455712Z digest=sha256:fd1213a9a1760d93f229a3645cb56c6d003e42747fbcd98fb10b47e3212ee8b6

Observation d96c49e9-21c3-486a-b975-1b6df707f5df · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:20.903754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:20.903754Z digest=sha256:3b49140d11faa2998afbc4605a75feedb4e71d81cc791f775061f12ddfae4971

Observation 165f3f58-950a-4ecc-972c-f5770c9a5fcd · outbound

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

End-to-End Differential Privacy in Training Deep Neural Network Classifiers Unlocking High-Accuracy Differentially Private Image Classification through Scale

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:19.931519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:19.931519Z digest=sha256:a1a215fc2149dcecdc30fcb7374a2bbfb27f7fbb473f8a03fd60debab7ab83c8

Observation a34267c1-6f2d-469c-980c-eb164a627db0 · outbound

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

End-to-End Differential Privacy in Training Deep Neural Network Classifiers Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:21.244346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:21.244346Z digest=sha256:2c4015dc388f0c0c23a92f59aaa2cb6112ab4c5b50941a7494b8fb67dba6d082

Observation 95899387-8bb9-4ac4-8938-1b8a886aa122 · outbound

This paper cites Netzer, T.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers Netzer, T

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-01T12:29:20.609900Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T12:29:20.609900Z digest=sha256:2227b4a1bf0754b3ab43a91d2b8bcabf6135f77d813798b0f8d3525f54b75db4

Pith citing papers

No inbound Pith citation observations are available.