DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems | Paper Citation Record

Observation db020502-d49e-4cf0-ac4b-0ee9dc46097b · outbound

This paper cites Get rid of your trail: Remotely erasing backdoors in federated learning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Get rid of your trail: Remotely erasing backdoors in federated learning

Reference 1

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Observation 51b6b12b-afa4-43ca-b021-2dab442a1ee8 · outbound

This paper cites Reconstructing training data with informed adversaries.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Reconstructing training data with informed adversaries

Reference 2

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DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Unresolved cited work

Reference 3

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Observation b8f62b6b-cd37-40b8-bcd5-a3657f08ede2 · outbound

This paper cites Morgenstern, Aaron Roth, and Steven Z.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Morgenstern, Aaron Roth, and Steven Z

Reference 4

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Observation af928aca-f8ea-4d92-afc3-f569a5d4eb57 · outbound

This paper cites When the curious abandon honesty: Federated learning is not private.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems When the curious abandon honesty: Federated learning is not private

Reference 5

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Observation 4065d6df-b067-4a01-9268-f457595566b8 · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems LEAF: A Benchmark for Federated Settings

Reference 6

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Observation 9e641c7a-45d2-4567-9c72-662c34421c2e · outbound

This paper cites Towards Making Systems Forget with Machine Unlearning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Towards Making Systems Forget with Machine Unlearning

Reference 7

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Observation 0782ec9b-f11d-49b7-be32-74ee53aca289 · outbound

This paper cites SPEAR:Exact Gradient Inversion of Batches in Federated Learning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems SPEAR:Exact Gradient Inversion of Batches in Federated Learning

Reference 8

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Observation 03959fbc-e577-4573-af31-e01eda02012e · outbound

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DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Unresolved cited work

Reference 9

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Observation 4999afd3-5877-437d-9493-6f2cc8dbd1f3 · outbound

This paper cites Data protection in the EU, 2024.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Data protection in the EU, 2024

Reference 10

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Observation 335b736e-a080-4709-853a-7bc12b8cc1a4 · outbound

This paper cites Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, and Tom Goldstein.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Fowl, Jonas Geiping, Wojciech Czaja, Micah Goldblum, and Tom Goldstein

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:21.186336Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Unresolved cited work

Reference 13

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Observation b43c2b36-26fb-4fb5-8943-dcea8acc61ca · outbound

This paper cites an unresolved cited work.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Unresolved cited work

Reference 14

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This paper cites an unresolved cited work.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Unresolved cited work

Reference 15

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Observation f0fda67f-b816-4d99-9015-1a251aea6bbb · outbound

This paper cites Towards General Deep Leakage in Federated Learning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Towards General Deep Leakage in Federated Learning

Reference 16

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This paper cites Deep Learning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Deep Learning

Reference 17

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Observation 14c9b893-d9f4-4039-ae56-bc59232f8c85 · outbound

This paper cites Generative Adversarial Networks.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Generative Adversarial Networks

Reference 18

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Observation 2fe93771-3d8b-41f4-ba3a-dff7a0b98cac · outbound

This paper cites Reconstruct- ing training data from trained neural networks.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Reconstruct- ing training data from trained neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:21.088931Z

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Observation 2d1bff47-ae2c-436d-b718-ffcd09ba8426 · outbound

This paper cites Federated Unlearning: How to Efficiently Erase a Client in FL?.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 20

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Observation fc676641-4e40-4838-b653-4b6a742d9d85 · outbound

This paper cites Deep residual learning for image recognition.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Deep residual learning for image recognition

Reference 21

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DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Unresolved cited work

Reference 22

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Observation 7c5e8df9-002e-4464-bb68-48326d72cab3 · outbound

This paper cites Deep models under the GAN: information leakage from collaborative deep learning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Deep models under the GAN: information leakage from collaborative deep learning

Reference 23

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Observation c4a2f801-49c5-4ee5-93e9-48011fade714 · outbound

This paper cites Learn what you want to unlearn: Unlearning inversion attacks against machine unlearning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Learn what you want to unlearn: Unlearning inversion attacks against machine unlearning

Reference 24

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Observation 83602abe-400d-4543-a013-5a8e5cf67221 · outbound

This paper cites Evaluating gradient inversion attacks and defenses in federated learning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Evaluating gradient inversion attacks and defenses in federated learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:21.055146Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7471257b-78b3-4b44-b2b5-66b89ab9796f · outbound

This paper cites Neural tangent kernel: Conver- gence and generalization in neural networks.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Neural tangent kernel: Conver- gence and generalization in neural networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:21.038026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1727ade1-9c7f-45e2-808c-ce093d941f0a · outbound

This paper cites Gradi- ent inversion with generative image prior.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Gradi- ent inversion with generative image prior

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:21.019014Z

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Observation cf72d387-f4b3-4507-b8f5-596420f9e29c · outbound

This paper cites Directional convergence and alignment in deep learning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Directional convergence and alignment in deep learning

Reference 28

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Observation 56ed68c2-89dc-43b6-932d-a74d2173b93c · outbound

This paper cites Forgettable Federated Linear Learning with Certified Data Unlearning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Forgettable Federated Linear Learning with Certified Data Unlearning

Reference 29

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Observation 8cddaa9c-3ad6-4339-9500-d9925aa86c39 · outbound

This paper cites Learning multiple layers of features from tiny images.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Learning multiple layers of features from tiny images

Reference 30

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DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Unresolved cited work

Reference 31

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Observation 3e420e45-85d8-41c0-ba2c-67c26b399caa · outbound

This paper cites Gradient-based learning applied to document recognition.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Gradient-based learning applied to document recognition

Reference 32

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Observation aa056f6d-858b-4045-b352-e47c8fedb5e6 · outbound

This paper cites Anti-backdoor learning: Training clean models on poisoned data.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Anti-backdoor learning: Training clean models on poisoned data

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.956193Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6cb046f0-8da2-4cab-a6d8-e681ea78e24d · outbound

This paper cites Deep gradient compression: Reducing the communication bandwidth for distributed training.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Deep gradient compression: Reducing the communication bandwidth for distributed training

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.937978Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 866a2db9-2286-4c09-84bf-95fd31eb99fa · outbound

This paper cites Federaser: Enabling efficient client-level data removal from federated learning models.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Federaser: Enabling efficient client-level data removal from federated learning models

Reference 35

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Observation fe59e966-4eca-4dc5-870b-a1d0096bf961 · outbound

This paper cites A survey on federated unlearning: Challenges, methods, and future directions.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems A survey on federated unlearning: Challenges, methods, and future directions

Reference 36

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Observation 0b247cd8-f7f8-449b-a831-1094027ec627 · outbound

This paper cites Gradient descent maximizes the margin of homogeneous neural networks.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Gradient descent maximizes the margin of homogeneous neural networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.920724Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 32c195c8-6661-474f-a395-11500fdf0170 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Communication-efficient learning of deep networks from decentralized data

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.902338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e6734520-71a6-4a8e-8ad7-817229a73898 · outbound

This paper cites Inf2guard: An information-theoretic framework for learning privacy-preserving representations against inference attacks.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Inf2guard: An information-theoretic framework for learning privacy-preserving representations against inference attacks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.883771Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 765190f6-208e-4b96-8e14-44f2440c0699 · outbound

This paper cites California Consumer Privacy Act (CCPA),.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems California Consumer Privacy Act (CCPA),

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.867591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 00151563-2d4b-4f8b-bd50-e75c7f8d6087 · outbound

This paper cites Rudin, Stanley Osher, and Emad Fatemi.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Rudin, Stanley Osher, and Emad Fatemi

Reference 41

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Observation 94e77340-c88b-483c-8776-d8ff67967159 · outbound

This paper cites Updates- leak: Data set inference and reconstruction attacks in online learning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Updates- leak: Data set inference and reconstruction attacks in online learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.835713Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f0828976-ed0e-4efd-a39f-8b306173da56 · outbound

This paper cites an unresolved cited work.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Unresolved cited work

Reference 43

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:41:20.851592Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c62dd606-e11d-4a23-9441-1469d4ea2db1 · outbound

This paper cites FRAMU: attention-based machine unlearning using federated reinforcement learning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems FRAMU: attention-based machine unlearning using federated reinforcement learning

Reference 44

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Observation 05a37c55-9b96-4d51-9318-1089414c7383 · outbound

This paper cites PRECODE - A generic model extension to prevent deep gradient leakage.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems PRECODE - A generic model extension to prevent deep gradient leakage

Reference 46

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Observation 9be06f2e-ca84-41a2-ba69-b02cc22c3ac8 · outbound

This paper cites Beyond inferring class representatives: User-level privacy leakage from federated learning.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Beyond inferring class representatives: User-level privacy leakage from federated learning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:41:14.576885Z

Source-reported events for the cited work

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Observation 17a65aff-7bcc-41fd-9c96-70ff8a47e96c · outbound

This paper cites Bovik, Hamid R.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Bovik, Hamid R

Reference 48

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Observation b32644fe-0e85-4b07-a538-615efed0084a · outbound

This paper cites BFU: bayesian federated unlearning with parameter self-sharing.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems BFU: bayesian federated unlearning with parameter self-sharing

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T11:41:14.483627Z

Source-reported events for the cited work

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Observation 7ce8a72e-876f-4d9a-a477-91edf907f7e9 · outbound

This paper cites Fishing for user data in large-batch federated learning via gradient magnification.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Fishing for user data in large-batch federated learning via gradient magnification

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.802304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 65f82a4f-2438-4861-aee0-1c9659eca33a · outbound

This paper cites Geiping, Liam Fowl, Micah Goldblum, and Tom Goldstein.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Geiping, Liam Fowl, Micah Goldblum, and Tom Goldstein

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.785650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 152d1e9f-fff3-4822-8699-a4c315cb2713 · outbound

This paper cites Reconstructing training data from model gradient, provably.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Reconstructing training data from model gradient, provably

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.819344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2bf2b33d-85f9-43d7-9b6e-e033d844be4e · outbound

This paper cites Álvarez, Jan Kautz, and Pavlo Molchanov.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Álvarez, Jan Kautz, and Pavlo Molchanov

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T11:41:15.447442Z

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Observation 8f57b6f7-1404-4482-9159-f6db193797d1 · outbound

This paper cites Federated unlearning: Guarantee the right of clients to forget.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Federated unlearning: Guarantee the right of clients to forget

Reference 55

Resolution: verified exact; doi, observed 2026-08-07T11:41:17.888809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 15d90fed-ecd5-480c-88e6-9f6fb15f4792 · outbound

This paper cites Compromise privacy in large-batch federated learning via malicious model parameters.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Compromise privacy in large-batch federated learning via malicious model parameters

Reference 56

Resolution: verified exact; doi, observed 2026-08-07T11:41:17.699928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1bce2871-dfe9-49a9-b72d-4540451298e8 · outbound

This paper cites iDLG: Improved Deep Leakage from Gradients.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems iDLG: Improved Deep Leakage from Gradients

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T11:41:15.909897Z

Source-reported events for the cited work

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Observation 0b0c121c-b6e8-489c-94b4-7dc10675fc55 · outbound

This paper cites Efros, Eli Shechtman, and Oliver Wang.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Efros, Eli Shechtman, and Oliver Wang

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T11:41:15.657484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 467bd209-5ca2-43d4-b009-5845f76db9ec · outbound

This paper cites Zhao, Atul Sharma, Ahmed Roushdy Elkordy, Yahya H.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Zhao, Atul Sharma, Ahmed Roushdy Elkordy, Yahya H

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T11:41:16.218937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a982c999-5f74-4d97-9bfa-118a0155664c · outbound

This paper cites HyperINF: Unleashing the hyperpower of the schulz’s method for data influence estimation, 2025.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems HyperINF: Unleashing the hyperpower of the schulz’s method for data influence estimation, 2025

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.768983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0647325d-9555-499e-bd8b-d1ca68bdaeee · outbound

This paper cites LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems LOKI: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation

Reference 61

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T11:41:17.589863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 37e1529c-c74f-4344-8c54-811f516f86e8 · outbound

This paper cites Blaschko.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Blaschko

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.752429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 91b450f0-5345-4c34-9732-cd343b6c6b7b · outbound

This paper cites Deep leakage from gradients.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Deep leakage from gradients

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.623569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cfd2a39a-924b-4ac1-82a0-1d1ab3f97fcf · outbound

This paper cites URL https://openreview.net/forum?id=RSU17UoKfJF.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems URL https://openreview.net/forum?id=RSU17UoKfJF

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.707049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 548ff3bb-9f31-494e-81ca-306e41e17946 · outbound

This paper cites Deep leakage from gradients.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Deep leakage from gradients

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.522863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 32f1a5cb-7a78-43fd-8752-73d76d6c9d79 · outbound

This paper cites Simplify using ∇θLu = H −1 r ∇θL(θ, xu, yu): Lsim = H −1 r − I ∇θL(θ, xu, yu) − J(xu)∆x − R 2 2.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Simplify using ∇θLu = H −1 r ∇θL(θ, xu, yu): Lsim = H −1 r − I ∇θL(θ, xu, yu) − J(xu)∆x − R 2 2

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.447836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation af4a8d63-2814-4e72-910e-5d2d253d1b40 · outbound

This paper cites Apply triangle inequality and use σmin(Ju)-lower bound (Assumption 7): L1/2 sim (T ) ≥ σmin(Ju)∥˜x(T ) u − xu∥2 − µxϵ(T ).

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems Apply triangle inequality and use σmin(Ju)-lower bound (Assumption 7): L1/2 sim (T ) ≥ σmin(Ju)∥˜x(T ) u − xu∥2 − µxϵ(T )

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:20.394650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 94a2d33b-9c9b-4e6b-9e9a-b0846d5aa329 · outbound

This paper cites URL https://openreview.net/forum?id=fwzUgo0FM9v.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems URL https://openreview.net/forum?id=fwzUgo0FM9v

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:41:21.169110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d07d789f-6073-462b-9298-81160c6c2b38 · outbound

This paper cites URL https://doi.org/10.1109/SP54263.

DRAUN: An Algorithm-Agnostic Data Reconstruction Attack on Federated Unlearning Systems URL https://doi.org/10.1109/SP54263

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T11:41:12.244636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers