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Paper Citation Record · LEDGER

Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2404.01231.

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

pith.paper-citation-record.v1
2404.01231 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:29:49.983629Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T09:25:40.834864Z

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 4cb37f0c-3d15-4aca-9a81-eb28e3e36c64 · inbound

Large Language Model Adversarial Landscape Through the Lens of Attack Objectives cites this paper.

Large Language Model Adversarial Landscape Through the Lens of Attack Objectives Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-09T10:29:49.983629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:29:49.983629Z digest=sha256:dae8b685817b125da38c8b6949d4e851e41788710b86fab074f6be3e26cc358c

Observation bc4530e6-94f6-4ced-878f-07503ae1f9d4 · inbound

Quantifying Cross-Modality Memorization in Vision-Language Models cites this paper.

Quantifying Cross-Modality Memorization in Vision-Language Models Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T10:27:49.683237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:27:49.683237Z digest=sha256:791938fce7f430a1bfa900fda1e1039c251d903bdff76b6143c95c8cb463420a

Observation 9e05af27-1a3e-4953-aa2b-6090027ce4e0 · inbound

A Systematic Review of Poisoning Attacks Against Large Language Models cites this paper.

A Systematic Review of Poisoning Attacks Against Large Language Models Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T05:59:33.836732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:59:33.836732Z digest=sha256:0aa1c87deea42eb3a4119d70b86e9f33e38b9d6d2fc7182277aca7af87e6a977

Observation a28e3ae1-e3bf-4518-9e47-725b547b0177 · inbound

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning cites this paper.

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T21:23:39.710949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:23:39.710949Z digest=sha256:0f70c46d5905ba38a79510c266326f3c31cf4f9f9f1b6e4c5d412865a9421aab

Observation 243ac1f8-b04f-4db5-83eb-4ca76a754df5 · inbound

UniAud: A Unified Auditing Framework for High Auditing Power and Utility with One Training Run cites this paper.

UniAud: A Unified Auditing Framework for High Auditing Power and Utility with One Training Run Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-06T19:55:22.345690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:55:22.345690Z digest=sha256:1f3bd047a3a6c0d235930c6cbb91ec4f2e7030de6b3f113a1b83cda8a4adef4f

Observation 0cc5d97b-10cb-49cc-9440-bcf92ddecb6f · inbound

Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications cites this paper.

Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

Reference 57

Resolution
malformed identifier
arxiv_id, observed 2026-06-30T17:24:57.252080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T17:20:16.735285Z digest=sha256:e8b0aeaba983bf0eaf239e9331379be8031185807399e627ca208c026958b1dc

Observation e546d5e6-17cb-41b3-96dc-848364ffa5c5 · inbound

Probing Memorization of Tabular In-Context Learning cites this paper.

Probing Memorization of Tabular In-Context Learning Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models

Reference 103

Resolution
verified exact
arxiv_id, observed 2026-07-01T09:25:40.837030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-07-01T06:37:44.328625Z digest=sha256:25e3341493d7327c9343a2894bf6b2f5623798c6b2a1e32472bf0ff25274e947