Paper Citation Record · LEDGER

Deep Learning with Gaussian Differential Privacy

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1911.11607.

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

pith.paper-citation-record.v1
1911.11607 v3

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 2026-08-16T06:30:59.297886+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:46:55.554181Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T23:35:07.409058Z

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 b173630c-3a7e-414f-8fc2-c0eab08ae30c · inbound

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee cites this paper.

Dyn-D$^2$P: Dynamic Differentially Private Decentralized Learning with Provable Utility Guarantee Deep Learning with Gaussian Differential Privacy

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T22:46:55.554181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9cc8bac-e447-4cb6-bf50-a526f186af0c · inbound

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD cites this paper.

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Deep Learning with Gaussian Differential Privacy

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-16T13:37:56.535077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f611b4c0-19fd-4c29-90ea-e321df3050d5 · inbound

Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds cites this paper.

Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds Deep Learning with Gaussian Differential Privacy

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:56:06.107933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 06bd4d31-d155-4647-96f8-21f4df379612 · inbound

Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds cites this paper.

Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds Deep Learning with Gaussian Differential Privacy

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-30T23:35:07.410459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8d35538f-412d-4d3a-b518-963a11379c45 · inbound

Differentially Private Consistent Release of Counting Queries cites this paper.

Differentially Private Consistent Release of Counting Queries Deep Learning with Gaussian Differential Privacy

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T08:05:47.963985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5900aaa6-fdaa-499c-aef2-6c347e8aae40 · inbound

Synthesizing Probabilistic Saturating Counters with Differentially Private Formal Guarantees cites this paper.

Synthesizing Probabilistic Saturating Counters with Differentially Private Formal Guarantees Deep Learning with Gaussian Differential Privacy

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T14:35:55.704285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.