Paper Citation Record · LEDGER

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization

As of 16 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2412.05767.

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

pith.paper-citation-record.v1
2412.05767 v3

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:28:27.864049Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:28:26.773061Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T20:28:28.020622Z

Reference resolution

37 of 37 outbound references displayed

verified exact1
verified fuzzy24
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9694ba5b-b17e-4218-b27a-197ab668995d · outbound

This paper cites DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:28:28.027288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 56f169a6-d1b7-485a-9396-27919a64da0a · outbound

This paper cites an unresolved cited work.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:28:29.183136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 476ea691-b9a5-4586-90ed-3e0b4accc600 · outbound

This paper cites an unresolved cited work.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:28:29.072331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2eb701e4-1c27-460c-b46d-dfde38b778ea · outbound

This paper cites DeMem can be seamlessly integrated into various adver- sarial training techniques.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization DeMem can be seamlessly integrated into various adver- sarial training techniques

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.984444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b20045a9-bdcb-4b44-af2f-ede856beff11 · outbound

This paper cites an unresolved cited work.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:28:28.967895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1f418cd8-787f-4162-9e5d-5714cfffc013 · outbound

This paper cites We then analyze individual samples to explain why DP can fail, followed by a detailed presentation of our proposed approach.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization We then analyze individual samples to explain why DP can fail, followed by a detailed presentation of our proposed approach

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.952275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f1919fcf-24e2-4087-8322-933855285a80 · outbound

This paper cites Setup Experiments were conducted on 8 NVIDIA 4090 GPUs using PyTorch [27].

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Setup Experiments were conducted on 8 NVIDIA 4090 GPUs using PyTorch [27]

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.937611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fe71f7b8-0018-4d1d-bef9-9cddf7f54da3 · outbound

This paper cites an unresolved cited work.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:28:28.923474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5da869c3-28a6-43c9-8c70-ce4d0942f23f · outbound

This paper cites An adversar- ial perspective on accuracy, robustness, fairness, and privacy: Multilateral-tradeoffs in trustworthy ml,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization An adversar- ial perspective on accuracy, robustness, fairness, and privacy: Multilateral-tradeoffs in trustworthy ml,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.908411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 860e5ab9-7c9a-49f4-9288-ba29187ad096 · outbound

This paper cites On the privacy risks of algorithmic fairness,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization On the privacy risks of algorithmic fairness,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.893575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dd726b66-94be-46d3-a7ba-b5030f7307a6 · outbound

This paper cites ADBM: Adversarial diffusion bridge model for reliable adversarial purification.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization ADBM: Adversarial diffusion bridge model for reliable adversarial purification

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T20:28:27.295060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4becd87-6d9c-491e-a82d-ca19f3b0112b · outbound

This paper cites Language-driven anchors for zero-shot adver- sarial robustness,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Language-driven anchors for zero-shot adver- sarial robustness,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.878189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7e124fb9-c07a-4460-ac9c-abc8edb50dd7 · outbound

This paper cites Faster-GCG: Efficient Discrete Optimization Jailbreak Attacks against Aligned Large Language Models.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Faster-GCG: Efficient Discrete Optimization Jailbreak Attacks against Aligned Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T20:28:27.305592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b6644c5-e48d-4037-a2ea-2b73d459274c · outbound

This paper cites Privacy risks of securing machine learning models against adversarial exam- ples,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Privacy risks of securing machine learning models against adversarial exam- ples,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.743082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 49167c93-1b49-4279-968c-d533da13cd33 · outbound

This paper cites On the privacy effect of data enhancement via the lens of memoriza- tion,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization On the privacy effect of data enhancement via the lens of memoriza- tion,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.645782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6e761f78-c522-4019-8a07-9e3b58a3c55d · outbound

This paper cites Membership inference attacks against machine learning models,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Membership inference attacks against machine learning models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.631643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c7623a50-6641-474d-8ba9-0a136ebabad3 · outbound

This paper cites Robustness Threats of Differential Privacy.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Robustness Threats of Differential Privacy

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:28:27.349811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e7f07a2-8904-4ddc-9cd6-e90065e347ac · outbound

This paper cites Learning to be adversarially robust and differentially private.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Learning to be adversarially robust and differentially private

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T20:28:27.355483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06637edf-cbf4-467d-b3fd-3d6f374684ba · outbound

This paper cites Deep learn- ing with differential privacy,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Deep learn- ing with differential privacy,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.617182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a300d9e6-3d1f-4004-abeb-faec7dcdaf28 · outbound

This paper cites Does learning require memorization? a short tale about a long tail,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Does learning require memorization? a short tale about a long tail,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.601673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bf4f2abc-8368-442f-850e-943eab575a70 · outbound

This paper cites What neural networks memorize and why: Discovering the long tail via influence es- timation,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization What neural networks memorize and why: Discovering the long tail via influence es- timation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.585849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 735d1115-6be5-42fd-95a7-afefe2f04d8b · outbound

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

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T20:28:27.395884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d78529ef-5011-42bf-a2e0-89e60d6e4fdb · outbound

This paper cites Theoretically principled trade-off between robustness and accuracy,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Theoretically principled trade-off between robustness and accuracy,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.569902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cea19936-b30f-4506-9d7b-bd297b20d286 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Explaining and Harnessing Adversarial Examples

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T20:28:27.413085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a54201c0-009f-4614-b3cb-e3f401ede91c · outbound

This paper cites Robust physical-world attacks on deep learning visual classification,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Robust physical-world attacks on deep learning visual classification,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.555173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d86f0e1e-25a6-426a-8169-272760189133 · outbound

This paper cites Adversarial weight perturbation helps robust generalization,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Adversarial weight perturbation helps robust generalization,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.538232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d51a13b8-fefe-4973-a5d5-864b1b5bf4bf · outbound

This paper cites Differential privacy,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Differential privacy,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.523629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5d743e88-1458-4b95-b327-2e9a22a140e7 · outbound

This paper cites Quality control of voice recordings in re- mote parkinson’s disease monitoring using the infinite hidden markov model,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Quality control of voice recordings in re- mote parkinson’s disease monitoring using the infinite hidden markov model,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.351377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fdc896c1-501f-4c11-8666-1801eef60dd0 · outbound

This paper cites Membership inference at- tacks from first principles,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Membership inference at- tacks from first principles,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.173052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 816b3943-1e7b-4a4e-8726-d325496a7c54 · outbound

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

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T20:28:27.463491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ecb4307-dae7-4b8f-af7d-54b9bdb62601 · outbound

This paper cites Privacy risk in machine learning: Analyzing the con- nection to overfitting,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Privacy risk in machine learning: Analyzing the con- nection to overfitting,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.119932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a16c81e9-5c27-4943-9bd4-4db28915e47d · outbound

This paper cites Systematic evaluation of pri- vacy risks of machine learning models,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Systematic evaluation of pri- vacy risks of machine learning models,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.104941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0fd258ca-86bf-4aac-a3db-d2b238c31571 · outbound

This paper cites On the difficulty of membership inference attacks,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization On the difficulty of membership inference attacks,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.088374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3eab1dd7-9d0c-4a51-bd5d-86ffd7f3d5de · outbound

This paper cites To trust or not to trust prediction scores for membership in- ference attacks,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization To trust or not to trust prediction scores for membership in- ference attacks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.074095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b78051cb-5f2f-4730-ba80-baf887459c5e · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Pytorch: An imperative style, high- performance deep learning library,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.058385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 69e6a893-a350-40ac-bda9-22f10eedf74c · outbound

This paper cites Deep residual learning for image recog- nition,.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Deep residual learning for image recog- nition,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:28:28.043475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation de82fb05-d003-4431-b3e6-51b46b5dabea · outbound

This paper cites Opacus: User-Friendly Differential Privacy Library in PyTorch.

DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T20:28:27.864049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9694ba5b-b17e-4218-b27a-197ab668995d · inbound