Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.06814.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T11:26:28.110630Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-09T22:49:16.381477Z
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 e3eab449-c5ed-4b7e-abf4-6468ce3ad117 · inbound
Trustworthy AI: Safety, Bias, and Privacy -- A Survey cites this paper.
Trustworthy AI: Safety, Bias, and Privacy -- A Survey Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning
Reference 70
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T11:26:28.110630Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 956f71f4-f9a0-4b05-aa0d-8a404ae15e54 · inbound
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective cites this paper.
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:42:28.979176Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dc5b29a5-998b-4c41-b7b6-699335959677 · inbound
Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach cites this paper.
Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-08-04T01:51:29.478938Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.