Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2102.12677.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:06:27.022546Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-08T22:15:39.221245Z
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 66118899-b1dd-4ad2-94e3-f6141651ead7 · inbound
AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks cites this paper.
AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:06:27.022546Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a90933c5-ce61-4c57-b65d-1d656b786a0d · inbound
DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models cites this paper.
DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T13:38:19.417947Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation eace2e30-9071-443c-95e9-265eb3b56e5d · inbound
Differentially Private Natural Gradient Descent cites this paper.
Differentially Private Natural Gradient Descent Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
Reference 45
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T22:15:39.223089Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.