Paper Citation Record · LEDGER

Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2212.06470.

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

pith.paper-citation-record.v1
2212.06470 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:05:02.656929Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:27:29.630639Z

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 3e6b039f-e0e9-4307-8682-b74301c7a1d4 · inbound

Scaling Laws for Differentially Private Language Models cites this paper.

Scaling Laws for Differentially Private Language Models Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-09T22:05:02.656929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f771b42-9e0b-4914-9246-bafd7217dd4e · inbound

Membership Inference Attacks for Unseen Classes cites this paper.

Membership Inference Attacks for Unseen Classes Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:24.194350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b0ba576-eaad-4d40-b354-44f418d9e43f · inbound

Lower Bounds for Public-Private Learning under Distribution Shift cites this paper.

Lower Bounds for Public-Private Learning under Distribution Shift Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T14:56:04.019949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b829cbb1-c65e-49d3-8ab2-673c196ec9d1 · inbound

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy cites this paper.

How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

Reference 226

Resolution: unresolved; no resolver link, observed 2026-08-03T18:53:07.147476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed980388-4ff5-4135-8691-aff3b0a8a30d · inbound

From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI cites this paper.

From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

Reference 127

Resolution: verified exact; arxiv_id, observed 2026-05-20T18:08:50.579312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f03528c-0e82-4825-82a6-bf99ca7735a1 · inbound

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models cites this paper.

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

Reference 212

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:27:29.631942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9bf6507f-7957-4e42-865c-33b0e91ae7d3 · inbound

Decomposing Memorization Reduction in Privacy-Preserving Fine-Tuning of SLMs for CSIRTs cites this paper.

Decomposing Memorization Reduction in Privacy-Preserving Fine-Tuning of SLMs for CSIRTs Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:25:48.770219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.