Paper Citation Record · LEDGER

Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2212.01539.

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

pith.paper-citation-record.v1
2212.01539 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:09:03.959979Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T13:37:56.506781Z

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 26d5f486-dbda-4261-adce-58f442d499ba · 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 Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T19:09:03.959979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 750e5d86-84f0-4c87-b4e0-f3f13ffecc64 · inbound

FlashDP: Private Training Large Language Models with Efficient DP-SGD cites this paper.

FlashDP: Private Training Large Language Models with Efficient DP-SGD Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T21:06:02.073011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2becb199-885b-4631-98ed-900c7cfbfe31 · inbound

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

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-16T13:37:56.509454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eb5d90cc-60b0-4e66-b134-5c3b19a9fdae · inbound

Mitigating Membership Inference in Intermediate Representations with Differentially Private Training cites this paper.

Mitigating Membership Inference in Intermediate Representations with Differentially Private Training Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:26:31.270195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.