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Paper Citation Record · LEDGER

Privacy Leaks by Adversaries: Adversarial Iterations for Membership Inference Attack

As of 18 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 2026-08-17T06:30:58.91139+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
  • metadata mismatch0

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 2026-08-17T06:30:58.91139+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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verified fuzzy
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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:26.660163Z digest=sha256:2258c88aac627c47f36883dece433ea57044ae61754c81cd0b34ccde733c61b4

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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source=pdf_text observed=2026-08-07T11:21:26.757606Z digest=sha256:5702370742d152b6cc5bee3504a9df48ca664dd617e6c94f810b238137b70715

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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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:26.815604Z digest=sha256:48976a890fa6397deeded5c9f7f3857858f1a6f215ada1529b78e390c5e73059

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=sha256:938c8301389a4ee71243be852635b8518a0dc229ddff542bf1a048a950dca980

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=sha256:3268f52da27acd2d8a5c8bdffe3c1bb8fbfacc4d966e5af50758ccb5ce8c2daf

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=pdf_text observed=2026-08-07T11:21:27.043524Z digest=sha256:ba39763a4910f092b16fb6d2f851d1cac608b2c66a090635c2cf46badf7a59a7

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:27.128838Z digest=sha256:b15e6d184412fbd371106bca9cbd0eb9d2e08dc11783e1d9b82f479433b7df2f

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:27.185180Z digest=sha256:8f3bb1a0f83f6a4cf8e8cc8dce3f98339c05ba7c4d34ca646e9b763c9ced73ed

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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raw_fallback, observed 2026-08-07T11:21:31.669969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:27.299806Z digest=sha256:e17934287206b3677962fb2159be3e2999d24c77526ff96e0725ef93b5d0ebe8

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:27.353339Z digest=sha256:cab850012d7bab96337afba9c0fbb51b8c9c7f3b5d8e7f223837035160ce4d4e

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=sha256:8c5fbac916f9a886912bc65da80f687ac127d157c5cc6c1757f5fe754f8fc375

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

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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:27.511666Z digest=sha256:8893a749a044f107aab31d1b3c4313cdfedd5d3d623f732007670ee4ee602ee6

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:27.576558Z digest=sha256:2491c71f2bebc66e26f501fc35a543999165b4ae2fa715a00171f5ce159d1974

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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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:27.641197Z digest=sha256:f916cbee480767667326703582f41204e3ee1071328a619816a4149499632ec3

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=sha256:5875b4d2c92ec1372e589442b584c05ac6f8f43d8d3956183fe2ee9b820fdfeb

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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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:27.836901Z digest=sha256:e823c5c1dc86ecd9d0308062cfc7c1382f20b50da66fc4d70ff3af5a28091d48

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

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source=pdf_text observed=2026-08-07T11:21:27.908079Z digest=sha256:7d6686e90c763b69715b1742097e79d6f04bcec9faded79ad13759c1cf19e781

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
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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:27.995647Z digest=sha256:2397df8e33f21d751fb6d17ee3d0aa8250b8e318b676851be0a2e812b9d01daf

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:28.060151Z digest=sha256:cff79ffc57624180e033ce20917849b96a5e41f05de067822a78906fb441c92e

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:e9b86c1a8572767f486a2157a4e37dbe5ee27cabf842cb660c11b62009c7ab25

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:d1a702f6b00314113583ab81d7ef835a2c17df35eba895d75eb43ee59d561cbf

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:28.323057Z digest=sha256:b94a665cc7a91d1a47f0180e57cfaf5ac85fd43b939f2db5facc6d3b68e75764

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=sha256:0d01bfc3ced91d205ea954329abc1c2a6a22983fdc5573c8c92d55764c523f5d

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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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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:28.510923Z digest=sha256:c07d31d1292a5a302d98c7e850368b899700c245a7e7af80e3e1a5c3dcd73ca4

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=sha256:86fa50b2fb3d4c0a65f4e8a4414597912d0b1b2c4405e103c4566059b33f0754

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:28.698855Z digest=sha256:59d8b7c42a80c00b04d986287b3732ca1deb3e8e9fc9d15fa41a85bfb070f708

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:28.817863Z digest=sha256:22439686de6aa9511faeafa7b2bada5644c0b52d67f6ce2b6e48500464ccb5cd

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:28.908942Z digest=sha256:bafc505b9fca6e048e538e956c3aad699f3143fda49c21804e8cbcb281daa018

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:28.996687Z digest=sha256:faf8d19b8664ff84a88d8b3c1bfc93674213126b896e9a13fa126a696fc9df86

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:29.059399Z digest=sha256:a437a4cfb7d61e3f7ee6126add1003a46d82da37a56fba5b14eba7e6bce3efed

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:29.129052Z digest=sha256:cd8ee6acf9090d0c8bf3186f4dbf98c56f8da4a4bbb6686f88cce91f9f07dd48

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-07T11:21:29.196502Z digest=sha256:f58435f1214c2bf6a68e98562e62c0f00a7c3ac74da89626667d263f807d1366

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:21:29.277094Z digest=sha256:e120b715ba066ef0ecf8abf38bcb334f5963aa7cbf924172707b93f213cfc327

Pith citing papers

No inbound Pith citation observations are available.