Paper Citation Record · LEDGER

Membership Inference Attacks for Unseen Classes

As of 23 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.06488.

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

pith.paper-citation-record.v1
2506.06488 v3

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:04:25.061599Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact2
verified fuzzy15
unresolved10
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cfe5bb5c-fdaf-416e-ac46-33455d0fedc0 · outbound

This paper cites Scalable membership inference attacks via quantile regression.Advances in Neural Information Processing Systems, 36:314–330, 2023.

Membership Inference Attacks for Unseen Classes Scalable membership inference attacks via quantile regression.Advances in Neural Information Processing Systems, 36:314–330, 2023

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:22.016374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 470d32b5-6e54-4aed-8c80-3d474129e102 · outbound

This paper cites Membership inference attacks from first principles.

Membership Inference Attacks for Unseen Classes Membership inference attacks from first principles

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:22.082887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a80a2acb-a08a-40f9-a05d-78db2dae928c · outbound

This paper cites Extracting training data from diffusion models.

Membership Inference Attacks for Unseen Classes Extracting training data from diffusion models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:30.941670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 10cf8af3-a111-4862-8f16-1f3ab96cbafe · outbound

This paper cites Label-only mem- bership inference attacks.

Membership Inference Attacks for Unseen Classes Label-only mem- bership inference attacks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:30.514604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ca7d307a-1b92-43bb-8e60-a0f7ca946f05 · outbound

This paper cites Are diffusion models vulnerable to membership inference attacks? InInternational Conference on Machine Learning, pages 8717–8730.

Membership Inference Attacks for Unseen Classes Are diffusion models vulnerable to membership inference attacks? InInternational Conference on Machine Learning, pages 8717–8730

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:30.151452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ecab6685-af40-4d3e-a08d-0eb9846db654 · outbound

This paper cites Strong membership inference attacks on massive datasets and (moderately) large language models.arXiv preprint arXiv:2505.18773, 2025.

Membership Inference Attacks for Unseen Classes Strong membership inference attacks on massive datasets and (moderately) large language models.arXiv preprint arXiv:2505.18773, 2025

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:22.444112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76bc1443-4f0e-4601-885a-2f81ed0e72dd · outbound

This paper cites Kim, Omer Reingold, and Guy N.

Membership Inference Attacks for Unseen Classes Kim, Omer Reingold, and Guy N

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:29.883610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 90b9d2c5-0b52-44bd-88f7-e804f21dbd2e · outbound

This paper cites On the societal impact of open foundation models.

Membership Inference Attacks for Unseen Classes On the societal impact of open foundation models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:29.610846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 89de9d46-f849-4196-8019-b5a138d2af1a · outbound

This paper cites Kim, Christoph Kern, Shafi Goldwasser, Frauke Kreuter, and Omer Reingold.

Membership Inference Attacks for Unseen Classes Kim, Christoph Kern, Shafi Goldwasser, Frauke Kreuter, and Omer Reingold

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:22.683898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d578ba8c-226b-4ce4-93f1-87ec036f9dc4 · outbound

This paper cites Stolen memories: Leveraging model memorization for calibrated {White-Box} membership inference.

Membership Inference Attacks for Unseen Classes Stolen memories: Leveraging model memorization for calibrated {White-Box} membership inference

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:29.301141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b653c3f6-779e-4cdf-b42d-192dad055569 · outbound

This paper cites Membership leakage in label-only exposures.

Membership Inference Attacks for Unseen Classes Membership leakage in label-only exposures

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:28.981142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b38344dc-c109-4781-9ef4-a19ad68f4c9f · outbound

This paper cites Membership inference attacks by exploiting loss trajectory.

Membership Inference Attacks for Unseen Classes Membership inference attacks by exploiting loss trajectory

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:28.693551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 91437237-2595-45b9-bc36-7fec49f5c505 · outbound

This paper cites Decoupled Weight Decay Regularization.

Membership Inference Attacks for Unseen Classes Decoupled Weight Decay Regularization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:23.004070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44019a4e-ff18-4c6f-83aa-1af84986746c · outbound

This paper cites Language models are unsupervised multitask learners.

Membership Inference Attacks for Unseen Classes Language models are unsupervised multitask learners

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:28.357169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f03d57b3-e5a2-4828-b964-ebbc4078a37e · outbound

This paper cites Uncertain: Modern topics in uncertainty quantification.https://www.cis.upenn.edu/ ~aaroth/uncertain.html, 2022.

Membership Inference Attacks for Unseen Classes Uncertain: Modern topics in uncertainty quantification.https://www.cis.upenn.edu/ ~aaroth/uncertain.html, 2022

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:28.022717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1fab8025-0a10-4cd3-a39c-476d160eea97 · outbound

This paper cites In2017 IEEE symposium on security and privacy (SP), pages 3–18.

Membership Inference Attacks for Unseen Classes In2017 IEEE symposium on security and privacy (SP), pages 3–18

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:27.768766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9438dc6b-4d58-4820-b100-e88f781aa4d3 · outbound

This paper cites Privacy risks of securing machine learning models against adversarial examples.

Membership Inference Attacks for Unseen Classes Privacy risks of securing machine learning models against adversarial examples

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:23.401494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d4cadad-8c48-49e7-add1-a81a24ac0ece · outbound

This paper cites Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing.

Membership Inference Attacks for Unseen Classes Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-07T06:04:25.854291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9b4beae1-8e2b-4e70-9f83-7309f76a49da · outbound

This paper cites Membership Inference Attacks on Diffusion Models via Quantile Regression.

Membership Inference Attacks for Unseen Classes Membership Inference Attacks on Diffusion Models via Quantile Regression

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-07T06:04:25.515231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7fe481b6-a535-467e-a52e-39dfd130e6db · outbound

This paper cites Identifying and eliminating csam in generative ml training data and models.Stanford Internet Observatory, Cyber Policy Center, 23:3, 2023.

Membership Inference Attacks for Unseen Classes Identifying and eliminating csam in generative ml training data and models.Stanford Internet Observatory, Cyber Policy Center, 23:3, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:27.482214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 22b7b743-b1e0-4c01-a135-7f0e621b88ec · outbound

This paper cites Generative ml and csam: Implications and mitigations.

Membership Inference Attacks for Unseen Classes Generative ml and csam: Implications and mitigations

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:27.105239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5ddfb4df-5300-418d-8f1b-c4fabe86a528 · outbound

This paper cites Safety by design for generative ai: Preventing child sexual abuse.

Membership Inference Attacks for Unseen Classes Safety by design for generative ai: Preventing child sexual abuse

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:26.752712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1f771b42-9e0b-4914-9246-bafd7217dd4e · outbound

This paper cites Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining.

Membership Inference Attacks for Unseen Classes Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:24.194350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93ded813-d668-44f8-9a34-230841773cc7 · outbound

This paper cites On the Importance of Difficulty Calibration in Membership Inference Attacks.

Membership Inference Attacks for Unseen Classes On the Importance of Difficulty Calibration in Membership Inference Attacks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:24.401244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd3f627f-381e-4ecf-a6d6-df9ffbf4bcd2 · outbound

This paper cites Privacy risk in machine learning: Analyzing the connection to overfitting.

Membership Inference Attacks for Unseen Classes Privacy risk in machine learning: Analyzing the connection to overfitting

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:24.585390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb4b2f47-1b06-44c3-b454-fbe6af49e1b1 · outbound

This paper cites Assessing membership inference attacks under distribution shifts.

Membership Inference Attacks for Unseen Classes Assessing membership inference attacks under distribution shifts

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T06:04:26.298079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f9eb6635-a510-4ee8-b5c9-566eb177b8ad · outbound

This paper cites Low-Cost High-Power Membership Inference Attacks.

Membership Inference Attacks for Unseen Classes Low-Cost High-Power Membership Inference Attacks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T06:04:24.893482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7d1ba9e-f3ba-4b3b-81e3-47f987ded3f4 · outbound

This paper cites Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data.

Membership Inference Attacks for Unseen Classes Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data

Reference 28

Resolution: malformed identifier; no resolver link, observed 2026-08-07T06:04:25.061599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.