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Paper Citation Record · LEDGER

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack

As of 8 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2506.02711.

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

pith.paper-citation-record.v1
2506.02711 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:21:29.277094Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

34 of 34 outbound references displayed

  • verified exact1
  • verified fuzzy17
  • unresolved16
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e1787c0e-4b59-4f26-9289-7bedacb67003 · outbound

This paper cites Superdriverai: Towards design and implementation for end-to-end learning-based autonomous driving.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Superdriverai: Towards design and implementation for end-to-end learning-based autonomous driving

Reference 1

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raw_fallback, observed 2026-08-07T11:21:32.775595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 82ab584f-2277-4465-9b69-9e2ec3170ddf · outbound

This paper cites Scalable membership inference attacks via quantile regression.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Scalable membership inference attacks via quantile regression

Reference 2

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raw_fallback, observed 2026-08-07T11:21:32.485902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:26.660163Z digest=sha256:12ab33428dc875278323b31e98441b8a8886d3aac9436dc829d4cd5c6655ba67

Observation 8c78805d-62f4-4024-98df-23f9225ba007 · outbound

This paper cites Towards evaluating the robustness of neural networks.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Towards evaluating the robustness of neural networks

Reference 3

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Unavailable: canonical work link unavailable.

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Observation 2962e0e2-ad65-4898-b73c-2487ff6544b6 · outbound

This paper cites The secret sharer: Evaluating and testing unintended memorization in neural networks.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack The secret sharer: Evaluating and testing unintended memorization in neural networks

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-07T11:21:32.158012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:26.815604Z digest=sha256:9964aae46a9fd0316d1a735b5c4e553d6cd4734dfb8db8bc65f0a96e1389eaca

Observation 8b741b13-5e8a-4207-b74e-454abb7f6fe2 · outbound

This paper cites Extracting training data from large language models.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Extracting training data from large language models

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:26.902277Z digest=sha256:90637b86fab8c6917b46ce96610388b8bb36ba4361cc67e306e01fa3e6e14014

Observation d09ba2fb-042f-4fe6-8e4f-ce4c4ef5ae75 · outbound

This paper cites Membership inference attacks from first principles.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Membership inference attacks from first principles

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:26.973546Z digest=sha256:7d00ebfdfb978d8fd4af6892b96cbb60ee0c6a157f8f208a009f0efc86c7c5bb

Observation f09e6cf1-81d8-44f6-b754-10564e897d49 · outbound

This paper cites Quantifying Memorization Across Neural Language Models.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Quantifying Memorization Across Neural Language Models

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:27.043524Z digest=sha256:1f2477bf83b15cb28eb329e226832a35a364e3bda09c5879bcc1921ed8e32bf2

Observation 2626b60c-88cb-417c-9cb1-fb7caa0463a2 · outbound

This paper cites Forecasting stock market crisis events using deep and statistical machine learning techniques.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Forecasting stock market crisis events using deep and statistical machine learning techniques

Reference 8

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raw_fallback, observed 2026-08-07T11:21:31.918601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:27.128838Z digest=sha256:2b562cfd2b16102f93b572ad252293fce10d31f3103c196a33ba310727ea9ea8

Observation 5ffc95f0-9fe0-4ca8-916f-32195d595dd3 · outbound

This paper cites Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning

Reference 9

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:27.185180Z digest=sha256:cb0d4d7260165cc02bfbcae41665567a70b93c975026bb86e13113d20ef570ba

Observation 2052be72-a05a-42fd-b833-8c7782e03fac · outbound

This paper cites Hopskipjumpattack: A query-efficient decision-based attack.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Hopskipjumpattack: A query-efficient decision-based attack

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:27.299806Z digest=sha256:819f273a262abd2cbdd708af929d759cdba91efd54f8242f54770be4adbdaacc

Observation fe0fecf2-7378-433c-8686-2f50d416b620 · outbound

This paper cites Amplifying membership exposure via data poisoning.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Amplifying membership exposure via data poisoning

Reference 11

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raw_fallback, observed 2026-08-07T11:21:31.393409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:27.353339Z digest=sha256:354bbf344ec618a3d7707ae6c270d46af55a392a396f58d8081b5a361b246d3e

Observation 2963c44b-63e9-41c3-b5bf-402b657fc3e8 · outbound

This paper cites Label- only membership inference attacks.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Label- only membership inference attacks

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:27.427820Z digest=sha256:12349d9c5b0783d73fd5c16fd773569eba3cf72936e6a50d99bf5123987a86fe

Observation f6f24906-e0d7-4f5d-8c73-0aad315fe8e1 · outbound

This paper cites Privacy side channels in machine learning systems.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Privacy side channels in machine learning systems

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:21:31.214282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:27.511666Z digest=sha256:48d56962b804ea786f6b793208d05c2a75e4928332607fa159f6aaf2a3382be2

Observation 49f355e1-c122-4594-b6a2-3b4803718aa8 · outbound

This paper cites Leveraging adversarial examples to quantify membership information leakage.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Leveraging adversarial examples to quantify membership information leakage

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-07T11:21:30.968902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:27.576558Z digest=sha256:70ef05b0ca84a41e7cab6e937593651a617433200cb006a35db3d1102a3d501c

Observation 501f2e2c-8bd8-4cdf-ba29-ade26bf496a9 · outbound

This paper cites Risk assessment for hospital readmissions: Insights from machine learning algorithms.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Risk assessment for hospital readmissions: Insights from machine learning algorithms

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-07T11:21:30.780209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:27.641197Z digest=sha256:953c7493391a554689f813c0b39f1cbd60c9ebd9b1f4cfc73d472f1c47d017a9

Observation 8f7d50a8-3839-4be8-9762-7e5ae4cee0bf · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Explaining and Harnessing Adversarial Examples

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:27.721455Z digest=sha256:1ac9595655f9d823d953f0421703445118c157617f962c56b906752594718d1c

Observation 43bc160c-fef2-41fe-87a7-9d55dc4ec032 · outbound

This paper cites Simple black-box adversarial attacks.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Simple black-box adversarial attacks

Reference 17

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verified fuzzy
raw_fallback, observed 2026-08-07T11:21:30.622004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:27.836901Z digest=sha256:1a3ad686057035c2d43f90f1c8efb145981d1c933df0719f06b70277db065a97

Observation 92d82447-3a68-4694-9abd-30f5d70b2bc4 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 18

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no resolver link, observed 2026-08-07T11:21:27.908079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 19f67cde-ddb1-411a-b0e7-84c776dcc5ba · outbound

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

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:21:30.422334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 4c6f8a47-624c-4e20-8f30-0a45c9a1c4c9 · outbound

This paper cites Scalable Extraction of Training Data from (Production) Language Models.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Scalable Extraction of Training Data from (Production) Language Models

Reference 20

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no resolver link, observed 2026-08-07T11:21:28.060151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:28.060151Z digest=sha256:7adc9d6c250fbc087e3c43aa19a15cd678b60adba3fa42ce393799c09cd208fc

Observation 6b9c9a84-b12e-4746-8ab4-625c4f860a33 · outbound

This paper cites Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:28.153750Z digest=sha256:98d0a7094efd5419658473ecccd1fa480af25f1cf99e6f6038a49ed1fddb5ad4

Observation dab98859-6eaa-4ba9-aa4f-5e7d5906cff2 · outbound

This paper cites ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 22

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source=pdf_text observed=2026-08-07T11:21:28.225597Z digest=sha256:7198f0f9664cf0ce06455bf66ea01e888267c7b71e502ef5dbd051bb7a028080

Observation 4197393e-a003-4c95-864b-613aa282e63b · outbound

This paper cites Machine learning as an early warning system to predict financial crisis.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Machine learning as an early warning system to predict financial crisis

Reference 23

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raw_fallback, observed 2026-08-07T11:21:30.297603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:28.323057Z digest=sha256:c5c21ddc1dbc3d6a7f02950d83e52fc30105dd35bde69ed9c4fe41c9a0112b70

Observation 487c3b25-c21d-48a1-8723-07f0dd20f1e5 · outbound

This paper cites Membership inference attacks against machine learning models.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Membership inference attacks against machine learning models

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:28.416923Z digest=sha256:243f0cf541b986a9a622c917488555462c69ca8df36e721f0ddabf65542ec2b5

Observation 373d3cdc-d5d3-4b1a-ae22-89df3d9ecb33 · outbound

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

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Systematic evaluation of privacy risks of machine learning models

Reference 25

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verified fuzzy
raw_fallback, observed 2026-08-07T11:21:30.182639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:28.510923Z digest=sha256:6af12db3f9d82537232ccf18e1bf6ab571587e13c51108fd4c7ded8621f61bcb

Observation 9e3d5234-e3e8-4de1-bf17-1588e212f934 · outbound

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

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Privacy risks of securing machine learning models against adversarial examples

Reference 26

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no resolver link, observed 2026-08-07T11:21:28.602654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:28.602654Z digest=sha256:eb6293cc3da2c54d68eb56e30510adc05793485e5834d6786e4e0603e5fcf2bf

Observation b1b8191f-f0d4-4ee9-bb94-a8b238cab2fa · outbound

This paper cites Understanding practical membership privacy of deep learning.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Understanding practical membership privacy of deep learning

Reference 27

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verified exact
raw_fallback, observed 2026-08-07T11:21:29.489880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:28.698855Z digest=sha256:c681f55adaa249680217c9cf8f0db1ec71819a7fb81069736ef43df9d02f1f10

Observation 7ba1fb16-7cc6-4a04-8732-5b9efe507c3b · outbound

This paper cites The Space of Transferable Adversarial Examples.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack The Space of Transferable Adversarial Examples

Reference 28

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no resolver link, observed 2026-08-07T11:21:28.817863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:28.817863Z digest=sha256:b67ce1abc758ad910727841094b2310f43667f603042c88cc5929aa99157a9eb

Observation 106b9f37-5a6e-4029-be1d-e59a81039090 · outbound

This paper cites Truth serum: Poisoning machine learning models to reveal their se- crets.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Truth serum: Poisoning machine learning models to reveal their se- crets

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-07T11:21:30.042193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:28.908942Z digest=sha256:24752612e1a42e446fcc8e88b175abd33e3334984cea060401278f1fe66e246d

Observation a4eede62-8fc0-4572-95d6-c4ace18822a7 · outbound

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

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack On the Importance of Difficulty Calibration in Membership Inference Attacks

Reference 30

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no resolver link, observed 2026-08-07T11:21:28.996687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:28.996687Z digest=sha256:003818cbfaaadb70da8dd5fb5a9d27c319c76139e6f409efee51ecd45ed96d5d

Observation 8c67cad9-bc14-45da-88f6-5a013cb1950d · outbound

This paper cites You only query once: An efficient label-only membership inference attack.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack You only query once: An efficient label-only membership inference attack

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-07T11:21:29.909376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:29.059399Z digest=sha256:3ebe1e5c6125e5846726827e4288aa26ea59466d0ceb5dd35028bb3270f7eece

Observation 34081adf-18b2-4977-8596-9ed6dedc701c · outbound

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

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Privacy risk in machine learning: Analyzing the connection to overfitting

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-07T11:21:29.782298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:29.129052Z digest=sha256:f607a154596f69c6b8d63e7981578db039ed9f5f3c8c2d54280490dd212185bd

Observation 57ce36cc-1ece-4534-9032-d98bc6e42155 · outbound

This paper cites Machine learning-based vehicle intention trajectory recognition and prediction for autonomous driving.

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Machine learning-based vehicle intention trajectory recognition and prediction for autonomous driving

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-07T11:21:29.672657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:21:29.196502Z digest=sha256:311111e978dd42673000316d56c309f254c5e61db8112bff3b5c7dfd37d1806b

Observation 50f9cd01-f6ce-467d-a6be-8d030bf9e15d · outbound

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

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack Low-Cost High-Power Membership Inference Attacks

Reference 34

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no resolver link, observed 2026-08-07T11:21:29.277094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:29.277094Z digest=sha256:2689bda02864a55e7d5fa4f6cae08ecb87e3926aafdd90f7aed202ace3823df4

Pith citing papers

No inbound Pith citation observations are available.