Paper Citation Record · LEDGER

On the Effectiveness of Regularization Against Membership Inference Attacks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2006.05336.

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

pith.paper-citation-record.v1
2006.05336 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:26:28.041506Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T07:35:28.946005Z

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 8b0e29eb-16e5-45d1-afac-6339bdf72b58 · inbound

Trustworthy AI: Safety, Bias, and Privacy -- A Survey cites this paper.

Trustworthy AI: Safety, Bias, and Privacy -- A Survey On the Effectiveness of Regularization Against Membership Inference Attacks

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T11:26:28.041506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fb467b9-f255-4526-bb6b-6127d1da313a · inbound

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation cites this paper.

SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation On the Effectiveness of Regularization Against Membership Inference Attacks

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T00:46:10.125502Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7098df97-7a1c-42fb-b870-2a5779540848 · inbound

Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set cites this paper.

Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set On the Effectiveness of Regularization Against Membership Inference Attacks

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:11:08.574108Z

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.

Observation c1545547-8fde-44dc-a762-ae06467116d7 · inbound

Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set cites this paper.

Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set On the Effectiveness of Regularization Against Membership Inference Attacks

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-07-01T07:35:28.947442Z

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.