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Paper Citation Record · LEDGER

Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2009.04872.

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

pith.paper-citation-record.v1
2009.04872 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:12:46.765116Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T11:34:10.408512Z

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 2a35f01a-b673-4ecb-a6eb-839e2162ce92 · inbound

Rethinking Membership Inference Attacks Against Transfer Learning cites this paper.

Rethinking Membership Inference Attacks Against Transfer Learning Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-10T18:12:46.765116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:12:46.765116Z digest=sha256:5fe8e602e8c61caa3043a4b7036f6cfa9e0cf3789320fd0a550a596ec354dc29

Observation 7664f312-1265-44d6-88ac-6df86ccba01f · inbound

Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription cites this paper.

Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-09T11:34:10.413342Z

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.

source=pdf_text observed=2026-08-09T11:34:08.365566Z digest=sha256:575ccb94a8a7e00592340ff5b2905077a7cdb517aee6e1f0f9272584d6d4bd73