Paper Citation Record · LEDGER

Generalization and Membership Inference Attack a Practical Perspective

As of 10 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2604.19936.

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

pith.paper-citation-record.v1
2604.19936 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T02:56:05.853278Z

measured 22 of 22 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 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

22 of 22 outbound references displayed

verified exact0
verified fuzzy21
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c236dd6c-9304-4f21-8f5b-5db69ff2d684 · outbound

This paper cites The secret sharer: Evaluating and testing unintended memorization in neural net- works.

Generalization and Membership Inference Attack a Practical Perspective The secret sharer: Evaluating and testing unintended memorization in neural net- works

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.393404Z

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.

Observation 44fe7ebf-273d-43a1-b0c3-307670c4f6a1 · outbound

This paper cites Extracting train- ing data from large language models.

Generalization and Membership Inference Attack a Practical Perspective Extracting train- ing data from large language models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.397976Z

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.

Observation 57690b55-61d7-4adc-8f53-67024f80cb6c · outbound

This paper cites Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning.

Generalization and Membership Inference Attack a Practical Perspective Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.403133Z

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.

Observation 8ee4ba39-a8fa-47f9-82c0-f25affdc54bb · outbound

This paper cites White-box vs black-box: Bayes optimal strategies for membership inference.

Generalization and Membership Inference Attack a Practical Perspective White-box vs black-box: Bayes optimal strategies for membership inference

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.400444Z

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.

Observation 6e27be39-08e1-4182-b99a-5ab910f01886 · outbound

This paper cites Systematic evaluation of privacy risks of machine learning models.

Generalization and Membership Inference Attack a Practical Perspective Systematic evaluation of privacy risks of machine learning models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.390271Z

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.

Observation 435ac5e4-c257-4157-8642-618f5ba4350e · outbound

This paper cites Stolen memories: Leveraging model memorization for calibrated white-box membership inference.

Generalization and Membership Inference Attack a Practical Perspective Stolen memories: Leveraging model memorization for calibrated white-box membership inference

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.384456Z

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.

Observation b91e5737-c609-45be-af1b-bc6a08a59c17 · outbound

This paper cites Information leaks in federated learn- ing.

Generalization and Membership Inference Attack a Practical Perspective Information leaks in federated learn- ing

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.372649Z

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.

Observation 6ae5573a-d38e-430f-927a-1c043563a906 · outbound

This paper cites Beyond model-level membership privacy leakage: an adversarial approach in federated learning.

Generalization and Membership Inference Attack a Practical Perspective Beyond model-level membership privacy leakage: an adversarial approach in federated learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.395872Z

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.

Observation 93afe4da-cc45-40ff-b381-5df129ed616c · outbound

This paper cites Membership inference attacks from first principles.

Generalization and Membership Inference Attack a Practical Perspective Membership inference attacks from first principles

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.375721Z

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.

Observation 1a07390c-8096-45dc-ac1c-e19014796a9d · outbound

This paper cites En- hanced membership inference attacks against machine learning models.

Generalization and Membership Inference Attack a Practical Perspective En- hanced membership inference attacks against machine learning models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.381714Z

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.

Observation b2144d8f-b309-43d9-9b88-e0ac9809791d · outbound

This paper cites Membership inference attacks against machine learning models.

Generalization and Membership Inference Attack a Practical Perspective Membership inference attacks against machine learning models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.378666Z

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.

Observation 08ba0065-cac6-4ba1-bc99-bb6fd610ffda · outbound

This paper cites Ml- leaks: Model and data independent membership inference attacks and defenses on machine learning models.

Generalization and Membership Inference Attack a Practical Perspective Ml- leaks: Model and data independent membership inference attacks and defenses on machine learning models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.387300Z

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.

Observation a277714d-8bc6-4004-9fd6-fcbbfb3e67bc · outbound

This paper cites Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning.

Generalization and Membership Inference Attack a Practical Perspective Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.363461Z

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.

Observation 5bb069c0-1e8e-44bb-af80-1480190bd3c5 · outbound

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

Generalization and Membership Inference Attack a Practical Perspective Privacy risks of securing machine learning models against adversarial examples

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.366103Z

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.

Observation 1be0c3a4-20f9-4a50-85cf-e1ccd7cc8779 · outbound

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

Generalization and Membership Inference Attack a Practical Perspective Privacy risk in machine learning: Analyzing the connection to overfitting

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.369665Z

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.

Observation eb265015-9425-4cbf-912b-cf2c788d1950 · outbound

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

Generalization and Membership Inference Attack a Practical Perspective On the Importance of Difficulty Calibration in Membership Inference Attacks

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:27.188244Z

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.

Observation 704a6b32-8ad9-419d-a099-1b59f0e58930 · outbound

This paper cites Revisiting membership inference under realistic assumptions.

Generalization and Membership Inference Attack a Practical Perspective Revisiting membership inference under realistic assumptions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.357869Z

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.

Observation 7536d6b8-9390-4d25-9683-33a769fd05ee · outbound

This paper cites A pragmatic approach to membership inferences on machine learning models.

Generalization and Membership Inference Attack a Practical Perspective A pragmatic approach to membership inferences on machine learning models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.345005Z

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.

Observation aa80d3b0-67c8-48e0-999f-eb41f1cdcb07 · outbound

This paper cites Label- only membership inference attacks.

Generalization and Membership Inference Attack a Practical Perspective Label- only membership inference attacks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.348152Z

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.

Observation cefc8fef-962e-4f1a-9425-105564d271ee · outbound

This paper cites Exploiting unintended feature leakage in collaborative learning.

Generalization and Membership Inference Attack a Practical Perspective Exploiting unintended feature leakage in collaborative learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.351720Z

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.

Observation 8186b7e9-d026-4af3-8450-fe304323cac9 · outbound

This paper cites Deep residual learning for image recognition.

Generalization and Membership Inference Attack a Practical Perspective Deep residual learning for image recognition

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.360887Z

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.

Observation 71542a5f-cdad-4c7a-8f68-975feb9522c6 · outbound

This paper cites Autoaug- ment: Learning augmentation policies from data.

Generalization and Membership Inference Attack a Practical Perspective Autoaug- ment: Learning augmentation policies from data

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:51:58.355100Z

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.

Pith citing papers

No inbound Pith citation observations are available.