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Paper Citation Record · LEDGER

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

As of 19 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2506.01777.

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

pith.paper-citation-record.v1
2506.01777 v1

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:41:17.477056Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

66 of 66 outbound references displayed

  • verified exact2
  • verified fuzzy27
  • unresolved34
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

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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source=pdf_text observed=2026-08-07T11:40:59.779124Z digest=sha256:d0bf36b933936f84c1f946b0aa3758e2fba1c83cc96231e5cbd9e251592dbe47

Observation 7e4e2f05-d372-4c50-964f-245c885230ba · outbound

This paper cites an unresolved cited work.

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

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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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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raw_fallback, observed 2026-08-07T11:41:21.239667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:40:59.941581Z digest=sha256:ee3325ae25b29e91d607fc1bc08f7dc679333d5db2f161c5ce850ae2dec86469

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:40:59.980548Z digest=sha256:ebddb7f898c90cfc39a15e5934319fcb8c8740a3ef6e9a00198deb0379d75c16

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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no resolver link, observed 2026-08-07T11:41:08.736791Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:41:08.736791Z digest=sha256:c49dc93ed3246d67e189dc223b793ae1e51d613cddf45f57ca96539d8604f37d

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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source=pdf_text observed=2026-08-07T11:41:09.160357Z digest=sha256:f814edb7be874c3d88c230772ad18fbdf04d2d9a4d593d4237422a1b9500e5fc

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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local_arxiv, observed 2026-08-07T11:41:18.107744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:09.289460Z digest=sha256:28214906eb2e23dc756bd1784eca3d34d0289ba9aea584fb6e0f227498e87253

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

This paper cites an unresolved cited work.

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

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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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

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation f30bfeb8-2f8f-4fd5-8795-1aefc30b136b · outbound

This paper cites an unresolved cited work.

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

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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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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raw_fallback, observed 2026-08-07T11:41:21.134083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 346eca3c-8f12-41cc-9099-92f202adc3e7 · outbound

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:10.222497Z digest=sha256:5af0974e362669014ef19abe912f0b6e91b4db3a8779811096db9e91c561ffec

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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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:41:10.341066Z digest=sha256:dd3a8978a1fe670308bf0c0ece79b8fdd22ac7e86e7daca0c1c9ab9df8754b86

Observation b23c8c2c-5dc2-42ef-b172-a1595add476e · outbound

This paper cites Deep Learning.

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

Reference 17

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source=pdf_text observed=2026-08-07T11:41:10.502979Z digest=sha256:8c78e1895748a7daefb5263b98e7ec58737e9210252da69fd1a7ce33099cc55d

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:10.669342Z digest=sha256:578d61d26e16364fc18ec66c3b275d21f411107d7133e521bf26fc524001153a

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

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raw_fallback, observed 2026-08-07T11:41:21.088931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:10.861108Z digest=sha256:efbbd528335a92ad07d85a8804a65a4893181208d0b180f792de4eaec9dc1fa3

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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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:41:11.076343Z digest=sha256:83f8c2213c7bdc69a98590badc89fdb3a54c1c994906539575fcc3915a29580a

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:11.280780Z digest=sha256:0cf86730d18354ad52ae66b81d9496b86fa577fe4d013384bfa1688e9ab318b4

Observation 2d4791a5-1367-4730-8be5-eb9adb68cf49 · outbound

This paper cites an unresolved cited work.

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

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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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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source=pdf_text observed=2026-08-07T11:41:11.711029Z digest=sha256:5bec5b6398fd83d69b191ddecba54419d03d481eb934933de91bf54ab732bd87

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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verified fuzzy
raw_fallback, observed 2026-08-07T11:41:21.071132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:11.959152Z digest=sha256:39001256f6a36226bd0d33d65f47a8fd711e3ed031bfd7b72ecfaf3350efa272

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

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:12.348760Z digest=sha256:7931cbdbf289b76cc3404bb5a38616d4bd6e366f03eaa961bb64b5cf3f87c235

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

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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 2026-08-19T06:32:44.657259+00:00.

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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

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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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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verified fuzzy
raw_fallback, observed 2026-08-07T11:41:21.000112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:12.711782Z digest=sha256:2677a0e1855ed52e4d567fd11cdccfd18be79051913a607959c54f078427ee2d

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:12.796431Z digest=sha256:af452642d91f47b6ff0b2c6dd0af2a3af7051701d17480cff561c8b60610373c

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:12.925974Z digest=sha256:df0666d79c37e27481c5a0c798007bd2cb3fa0c53903c123661cc62229686636

Observation 3036ac56-865c-4547-a0ba-3bde79b7a4ab · outbound

This paper cites an unresolved cited work.

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

Reference 31

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unresolved
raw_fallback, observed 2026-08-07T11:41:20.972061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:13.026277Z digest=sha256:7570fa98e1f1c24250f2405a5be881af39bc5d017a163d394746e3b2f4a5d526

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:13.150189Z digest=sha256:29b1b44409aca8bbc1e4cab245e0cca2bb307ced0b317b5c8ac3e17b15939ac8

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:13.239031Z digest=sha256:694e7060d454e5dcc22b1e245202b760471d0cfe0a5bd10e1ec27e99520b2a5a

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:13.336008Z digest=sha256:0b89b17fb11f0e07889f137328d5e7f208061999619727a25ecae71df157d413

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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no resolver link, observed 2026-08-07T11:41:13.466222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:13.466222Z digest=sha256:2cdb678b085cfa09a3aad026c8ab767aed2e189db6646791742b92b45e88e25d

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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no resolver link, observed 2026-08-07T11:41:13.573915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:13.573915Z digest=sha256:d72e4378288a87fa2e1febf8d368a29d7c5bc12180e9347b6454d64cdf71553f

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:13.647896Z digest=sha256:267d096227d55bf135497f9b2ec88c1ad09ae85117b74c123cbf10e46653c354

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:13.683346Z digest=sha256:86c856452688530931fa057fc4fc201cb733975712717218a23f1e5a76d970e1

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:13.760113Z digest=sha256:d2d416527fe4e4dc7bf11193a4f11ed6a20e67ccb887581a1770f7d1022c8bf0

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:13.856391Z digest=sha256:334eb0278937946e8b48f0f741c8ff3cf49a40bd62df7d77d05a166414172c65

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

Resolution
unresolved
no resolver link, observed 2026-08-07T11:41:14.018235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:14.018235Z digest=sha256:e0b4cbd6f046af23747fb75f5a674436c72320bc1001b66dd8dcd76dc98d99c3

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:14.144642Z digest=sha256:b86cb324e93473653f9e781efbc422ab697abb2d999ce03385dcb835d72643ce

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:13.934970Z digest=sha256:ad6a6ec5e7faa71b549c775aca3a1ec0030002e7ae67db42e44eb8e2adea301e

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

Resolution
unresolved
no resolver link, observed 2026-08-07T11:41:14.301384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:14.301384Z digest=sha256:c1fe3b9fd0eb1942f5e3cdba5d66d6f8e57f4f6e71e324f2fad4e1ff9dc022f9

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

Resolution
unresolved
no resolver link, observed 2026-08-07T11:41:14.226065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:14.226065Z digest=sha256:e9cfdd7e95b0ed7c7af1b6c85fd9e41dce98dbae8b1bd0c60229ecfbc68053a7

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:14.576885Z digest=sha256:29d622020a68d011eec336510c73442de7b8e82ed43729554fcace91fccb2589

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

Resolution
unresolved
no resolver link, observed 2026-08-07T11:41:14.652691Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:14.652691Z digest=sha256:40d6d749172818625b5a51e503cdde30be184f7ca180936e8d165fcb2c81d96b

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:14.483627Z digest=sha256:dc226ecc93a26def28ff4c012e7e7d340eaf331f6209b448724bcae8b9ab39b9

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:14.830977Z digest=sha256:e8a4457730b05b662e2cd290111a686dcf961bc79ae223d8fd37cc712c9db721

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:14.973660Z digest=sha256:a699a4075c7a17e8d47cdb3989b5fe57dd35c9d31be98a346e6063ed176621eb

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:14.746305Z digest=sha256:7fa82acbb212d26509a8ada5415dacd67cde72368cb82b7bc02919f244c92d57

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:15.447442Z digest=sha256:ea07f996e8afdf0bc267a9ef9a1377dc9f97fc089c00c0bf989d81e80bedaa49

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:15.131103Z digest=sha256:0a29759a1885e6565d120926125e7797bd89a6268a48e4a40fa367c57cad835f

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:15.776785Z digest=sha256:a5f476bcd4d35bfcf8702e25a37dbdb07764f7135d2a72c0971c201849e51244

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:41:15.909897Z digest=sha256:5fe1262f1cffd730ba57ca4838d2a3eebf2f758a1f16e6cf4440ab10115ded6a

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.

source=pdf_text observed=2026-08-07T11:41:15.657484Z digest=sha256:0856f1e8b2bf964670d234bba78621f13a9d06d8bfdd6c9fadcf782216e929df

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.

source=pdf_text observed=2026-08-07T11:41:16.218937Z digest=sha256:d856f9d19f7fb3b049eb71b196dd58a963475da01c8e57601ebb725861a2e834

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:16.400644Z digest=sha256:c4bc24ab511bbefbe9d99063a190ef52e2570d02e8eec5de60c481bc1893dc1a

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:16.075853Z digest=sha256:a8adfe5ec266f916987b8a7d45fca47e5f4c1f86bb1e1a42395764ad11ba3d83

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:16.850232Z digest=sha256:f790cb1c13fc9f84e14b49c5115c27f35b7dbaf8d148c53966956e75522fadaf

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:17.040378Z digest=sha256:b9d2e0b06d1b2805741b834bc7913aacc3de3f60c77837061c210bfc8014eaf6

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:16.921036Z digest=sha256:321337acb9ebd7a08e580bf1c2daffeeb7f693410f8daaea20a55dd5e6fbb567

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:17.140947Z digest=sha256:44c78c7040ee9863b93356056037128b55a67da73fd9d7c150a0c032b302e10b

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:17.312576Z digest=sha256:3b158e31387000c15e4f32c6545148289c6f58615b33e052dded26be66049d1c

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:17.477056Z digest=sha256:d96f7c7937048455aa552ecd28617d944585c1eb850fca6b456f0b35830e782d

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:41:09.758303Z digest=sha256:c80128591f0c65ccc9237775221c05d82f4ac2d7f8376cd97a9e19e3dd6ce4d5

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.

source=pdf_text observed=2026-08-07T11:41:12.244636Z digest=sha256:ef3522a5b16ec498eaacf4b3639efe192d3263f2e9a528e7cf079dcd4b4e8e4d

Pith citing papers

No inbound Pith citation observations are available.