Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.09540.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T08:06:45.822258Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-09T10:06:09.389744Z
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 4b2022c2-ec28-47d7-8501-e2c7dd59bced · inbound
On Privacy Leakage in Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics cites this paper.
On Privacy Leakage in Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T05:05:57.278349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1695852b-967b-4100-b41e-de550a2910d0 · inbound
Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data cites this paper.
Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
Reference 43
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T10:06:09.392293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b7fec964-3b39-45d2-a8c8-92f4b69b3988 · inbound
Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data cites this paper.
Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T08:06:45.822258Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.