Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
InstaHide: Instance-hiding Schemes for Private Distributed Learning
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2010.02772.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T18:06:32.284436Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
19
pith, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 89a9b43b-1ab7-4aab-b092-685898cf2370 · inbound
BlindFL: Segmented Federated Learning with Fully Homomorphic Encryption cites this paper.
BlindFL: Segmented Federated Learning with Fully Homomorphic Encryption InstaHide: Instance-hiding Schemes for Private Distributed Learning
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T18:06:32.284436Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 45394e57-6723-4518-933f-9eb2d00f6bf4 · 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 InstaHide: Instance-hiding Schemes for Private Distributed Learning
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-07-31T22:50:59.390819Z
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 d57876b5-d4d9-4660-a8ec-6c4a7723df9d · 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 InstaHide: Instance-hiding Schemes for Private Distributed Learning
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T01:42:10.218832Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.