Paper Citation Record · LEDGER

R-GAP: Recursive Gradient Attack on Privacy

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2010.07733.

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

pith.paper-citation-record.v1
2010.07733 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:06:28.062115Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T04:39:34.976590Z

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 a2f05dba-2408-48c4-aad4-9d3db28e9330 · inbound

Gradient Inversion Transcript: Leveraging Robust Generative Priors to Reconstruct Training Data from Gradient Leakage cites this paper.

Gradient Inversion Transcript: Leveraging Robust Generative Priors to Reconstruct Training Data from Gradient Leakage R-GAP: Recursive Gradient Attack on Privacy

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T14:06:28.062115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b46e39da-8938-4aef-a4c0-f741e283aec4 · 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 R-GAP: Recursive Gradient Attack on Privacy

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T19:06:27.029681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9652ce0-db62-48f0-8105-a53bd3ca5d4c · inbound

Images in Motion?: A First Look into Video Leakage in Collaborative Deep Learning cites this paper.

Images in Motion?: A First Look into Video Leakage in Collaborative Deep Learning R-GAP: Recursive Gradient Attack on Privacy

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T19:43:59.958179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f8c2f12-45fe-41f9-a9e7-b5649fd1ee01 · inbound

FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs cites this paper.

FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs R-GAP: Recursive Gradient Attack on Privacy

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:30:51.398767Z

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 f6b124ff-64b1-41a1-85cb-54bb23a6b5cf · inbound

Predictability as a Fine-Grained Measure for Privacy cites this paper.

Predictability as a Fine-Grained Measure for Privacy R-GAP: Recursive Gradient Attack on Privacy

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:49:30.891174Z

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 a10da651-e929-47a1-9099-1e9c9c3369f8 · inbound

From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning cites this paper.

From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning R-GAP: Recursive Gradient Attack on Privacy

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:39:34.978442Z

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 7a0db588-3487-48d9-b37c-0e2908ec646c · inbound

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement cites this paper.

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement R-GAP: Recursive Gradient Attack on Privacy

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-31T22:47:14.281362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8f8bd53-62c2-465e-a185-9df8b85a7f6c · inbound

TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement cites this paper.

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T01:42:10.871511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.