Paper Citation Record · LEDGER

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks

As of 23 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2606.25589.

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

pith.paper-citation-record.v1
2606.25589 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-25T21:09:23.769302Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

47 of 47 outbound references displayed

verified exact4
verified fuzzy0
unresolved43
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 277b31e0-b03a-42fc-b821-cebd104110fa · outbound

This paper cites Graph convolutional networks: a comprehensive review,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Graph convolutional networks: a comprehensive review,

Reference 1

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Observation be10a78d-8a51-4313-8e7c-c7e2ecf83854 · outbound

This paper cites Benchmarking backdoor attacks on graph convolution neural networks: A comprehensive analysis of poisoning techniques,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Benchmarking backdoor attacks on graph convolution neural networks: A comprehensive analysis of poisoning techniques,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

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Observation e130cf96-ff80-4828-bddb-9f35be1bf7c6 · outbound

This paper cites Circuit-gnn: Graph neural networks for distributed circuit design,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Circuit-gnn: Graph neural networks for distributed circuit design,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4571ee0d-4f5a-4d73-bb72-29db448d3c71 · outbound

This paper cites Gnn-based hierarchical annotation for analog circuits,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Gnn-based hierarchical annotation for analog circuits,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 367eeaf0-bb57-4299-8605-487891b7fca5 · outbound

This paper cites Graph of circuits with gnn for exploring the optimal design space,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Graph of circuits with gnn for exploring the optimal design space,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b8e114b-8761-4935-b1f3-2bd4cca1900f · outbound

This paper cites Trustworthy graph neural networks: Aspects, methods, and trends,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Trustworthy graph neural networks: Aspects, methods, and trends,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 158662fc-3b55-4576-b97a-1bb924e5e06e · outbound

This paper cites Poisonedgnn: Backdoor attack on graph neural networks-based hardware security systems,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Poisonedgnn: Backdoor attack on graph neural networks-based hardware security systems,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5447d833-185f-4bf6-bb31-c50abdb4f0db · outbound

This paper cites Graph neural networks: a survey on the links between privacy and security,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Graph neural networks: a survey on the links between privacy and security,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4479ea99-2f46-418e-82f9-87cefe809d6d · outbound

This paper cites Deep leakage from gradients,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Deep leakage from gradients,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d68786a-fc9c-4c22-ad48-4f71296ab7c0 · outbound

This paper cites Gradient leakage attacks in federated learning,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Gradient leakage attacks in federated learning,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7e1be83-292d-4aa7-b758-0dfa57294d5b · outbound

This paper cites Dropout is not all you need to prevent gradient leakage,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Dropout is not all you need to prevent gradient leakage,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

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Unavailable: canonical work link unavailable.

Observation 7eda2f04-5d04-44a4-b405-a7d9b9af63f6 · outbound

This paper cites Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:06.667431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 28737a1d-6a0b-4b63-a635-34ecae9c7f73 · outbound

This paper cites Graphsage-based multi-path reliable routing algorithm for wireless mesh networks,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Graphsage-based multi-path reliable routing algorithm for wireless mesh networks,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

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Observation 47c099bd-9013-4ab8-bba1-4987cdbd9840 · outbound

This paper cites Omla: An oracle- less machine learning-based attack on logic locking,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Omla: An oracle- less machine learning-based attack on logic locking,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5411455f-5b20-47bb-990b-18d2bd42fce4 · outbound

This paper cites Parsing netlists of integrated circuits from images via graph attention network,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Parsing netlists of integrated circuits from images via graph attention network,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

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Observation 2089d78e-a11a-44e6-b074-16d1b8571e55 · outbound

This paper cites Trojansaint: Gate-level netlist sampling-based inductive learning for hardware trojan detection,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Trojansaint: Gate-level netlist sampling-based inductive learning for hardware trojan detection,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

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Observation f80d426a-9f28-4daf-86bf-9afb64723f85 · outbound

This paper cites Defense against adversarial attacks via controlling gradient leaking on embedded manifolds,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Defense against adversarial attacks via controlling gradient leaking on embedded manifolds,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1332fb63-c26a-4b10-9d3f-aeef8f22c438 · outbound

This paper cites Gradient leakage attack resilient deep learning,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Gradient leakage attack resilient deep learning,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98edb0ab-f168-49d0-9c10-83b73392ee39 · outbound

This paper cites Breaking secure aggregation: Label leakage from aggregated gradients in federated learning,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Breaking secure aggregation: Label leakage from aggregated gradients in federated learning,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee2f5692-ea17-4cff-a708-e4f8a6f9f550 · outbound

This paper cites Model compression hardens deep neural networks: A new perspective to prevent adversarial attacks,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Model compression hardens deep neural networks: A new perspective to prevent adversarial attacks,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53ce6e63-3aad-46ae-9710-faef7ed6018b · outbound

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

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Deep models under the gan: information leakage from collaborative deep learning,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a2e637c-b6bb-4fa8-915d-1c0828c246e4 · outbound

This paper cites Inverting gradients-how easy is it to break privacy in federated learning?.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Inverting gradients-how easy is it to break privacy in federated learning?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ba7094e-8052-4aa7-9500-b9afa273b086 · outbound

This paper cites Exploiting unintended feature leakage in collaborative learning,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Exploiting unintended feature leakage in collaborative learning,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10ba6641-0fb0-4d4b-92cc-3e5ec307d4cc · outbound

This paper cites A Survey on Gradient Inversion: Attacks, Defenses and Future Directions.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks A Survey on Gradient Inversion: Attacks, Defenses and Future Directions

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:06.647094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b0ff1376-5a89-4fb8-8519-e121b38b7278 · outbound

This paper cites Gradient Inversion Attack on Graph Neural Networks.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Gradient Inversion Attack on Graph Neural Networks

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:40:06.641515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 88160741-b376-4f2d-a10f-463eeae98075 · outbound

This paper cites Everything is connected: Graph neural networks,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Everything is connected: Graph neural networks,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

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Observation 4686c5f9-6f57-4adb-ab55-e6d0a0765975 · outbound

This paper cites Mathematical expres- siveness of graph neural networks,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Mathematical expres- siveness of graph neural networks,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

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Observation eb1e78df-a0fe-4c7d-929c-bc282465f879 · outbound

This paper cites Vision gnn: An image is worth graph of nodes,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Vision gnn: An image is worth graph of nodes,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dadb388-fb71-43d8-bd8f-4ce7e4b5c316 · outbound

This paper cites Graph neural network via edge convolution for hyperspectral image classification,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Graph neural network via edge convolution for hyperspectral image classification,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

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Observation 8012095c-299b-4d6a-a613-951167cbd324 · outbound

This paper cites Graphs, convolutions, and neural networks: From graph filters to graph neural networks,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Graphs, convolutions, and neural networks: From graph filters to graph neural networks,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

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Observation 02fc3fe2-44b2-4172-aa5d-356ce23159fa · outbound

This paper cites Membership inference attacks on machine learning: A survey,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Membership inference attacks on machine learning: A survey,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

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Observation c07dd80d-cfe9-467d-9b9d-ee945d8fe1e0 · outbound

This paper cites Advances in logic locking: Past, present, and prospects,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Advances in logic locking: Past, present, and prospects,

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d681bf70-2dab-4c62-b12f-df3ff2e0cc05 · outbound

This paper cites A survey of the implementations of model inversion attacks,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks A survey of the implementations of model inversion attacks,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ceeb7cb-8d61-4400-ab8a-a078d40382e2 · outbound

This paper cites An automated framework for board-level trojan benchmarking,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks An automated framework for board-level trojan benchmarking,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

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Observation 5001f264-59a1-4b41-81ee-d740145dc349 · outbound

This paper cites The state-of-the-art in ic reverse engineering,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks The state-of-the-art in ic reverse engineering,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

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Unavailable: canonical work link unavailable.

Observation d4a3ccf0-f221-4627-9964-9e1664703d07 · outbound

This paper cites Netlist reverse engineering for high- level functionality reconstruction,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Netlist reverse engineering for high- level functionality reconstruction,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

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Observation 823f1d5b-d474-4ed6-aea2-cda2f4cd3e67 · outbound

This paper cites Adaptivenet: Post-deployment neural architecture adaptation for diverse edge environments,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Adaptivenet: Post-deployment neural architecture adaptation for diverse edge environments,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df4833af-b1da-48d9-bc28-9aad39e0a47b · outbound

This paper cites Side channel attacks for architecture extraction of neural networks,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Side channel attacks for architecture extraction of neural networks,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

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Observation 26b0bc29-4b56-47d9-94b6-3bec6c672d58 · outbound

This paper cites Deep learning with differential privacy,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Deep learning with differential privacy,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

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Observation e1b17dbe-0081-4803-b75b-8dabb4dc0224 · outbound

This paper cites Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-07-04T19:40:06.644302Z

Source-reported events for the cited work

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Observation 1d82e810-a7b1-45ce-afa0-debbb6f08327 · outbound

This paper cites A survey of handwritten character recognition with mnist and emnist,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks A survey of handwritten character recognition with mnist and emnist,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2985c089-6951-4427-a8d3-13bdd66533ec · outbound

This paper cites Unveiling the iscas-85 benchmarks: A case study in reverse engineering,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Unveiling the iscas-85 benchmarks: A case study in reverse engineering,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6deeccde-4527-4650-a55d-19591a996d82 · outbound

This paper cites The epfl combinational benchmark suite,.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks The epfl combinational benchmark suite,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5184d251-c7ce-4024-8856-afe13dad92a2 · outbound

This paper cites This transformation limits the granularity of feature updates, reducing inversion fidelity.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks This transformation limits the granularity of feature updates, reducing inversion fidelity

Reference 44

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d8c2e4f-4980-43ac-8de9-94c9251ab3f5 · outbound

This paper cites This process eliminates weak connections in the NN, making gradient inversion less effective.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks This process eliminates weak connections in the NN, making gradient inversion less effective

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 549f82ed-0c67-4af5-9e92-51b40b15a4f9 · outbound

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Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks Unresolved cited work

Reference 46

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

Source-reported events for the cited work

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Observation 21555831-d35b-4a7c-a036-a6986d696955 · outbound

This paper cites This forces the model to optimize for robustness rather than merely fitting the clean training data.

Leaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks This forces the model to optimize for robustness rather than merely fitting the clean training data

Reference 47

Resolution: unresolved; no resolver link, observed 2026-06-25T21:09:23.769302Z

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Pith citing papers

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