Comparing privacy notions for protection against reconstruction attacks in machine learning | Paper Citation Record

Observation 992554aa-15d2-4964-a3f6-30cd50eb26ff · outbound

This paper cites The algorithmic foundations of differential privacy,.

Comparing privacy notions for protection against reconstruction attacks in machine learning The algorithmic foundations of differential privacy,

Reference 1

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Observation b99893d6-8ac2-4b4a-af71-9e546c96f055 · outbound

This paper cites Broadening the scope of differential privacy using metrics,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Broadening the scope of differential privacy using metrics,

Reference 2

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Observation 6ddab337-f5e5-4433-99a5-7b80e8cb0d5a · outbound

This paper cites Stochastic gradient descent with differentially private updates,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Stochastic gradient descent with differentially private updates,

Reference 3

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Observation ff1324bf-c3ad-45a6-a13d-42e07e2e1ac7 · outbound

This paper cites Deep learning with differential privacy,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Deep learning with differential privacy,

Reference 4

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Observation 7223cf17-5b59-41e0-9dba-cf5740f68011 · outbound

This paper cites Back to the drawing board: Revisiting the design of optimal location privacy-preserving mechanisms,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Back to the drawing board: Revisiting the design of optimal location privacy-preserving mechanisms,

Reference 5

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Observation 39d0bef5-09db-477f-87ef-43b137674197 · outbound

This paper cites Privic: A privacy-preserving method for incremental collection of location data,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Privic: A privacy-preserving method for incremental collection of location data,

Reference 6

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Observation ed0a76c5-12ec-4b84-aa7a-7b7842a0617e · outbound

This paper cites A privacy- preserving querying mechanism with high utility for electric vehicles,.

Comparing privacy notions for protection against reconstruction attacks in machine learning A privacy- preserving querying mechanism with high utility for electric vehicles,

Reference 7

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Observation 13bcfbf2-eb58-4bba-b6c1-0e64ffeb1998 · outbound

This paper cites Differentially private obfuscation of facial images,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Differentially private obfuscation of facial images,

Reference 8

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Observation e2040043-f88f-44f7-b593-23d905da1190 · outbound

This paper cites Differentially private facial obfuscation via generative adversarial networks,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Differentially private facial obfuscation via generative adversarial networks,

Reference 9

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Observation 73e3175f-f742-4b2a-ac20-ff951e1d6666 · outbound

This paper cites Generalised differential privacy for text document processing,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Generalised differential privacy for text document processing,

Reference 10

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Observation a336340d-3316-4f34-9af1-a52802742b6e · outbound

This paper cites Leveraging hierarchical representations for preserving privacy and utility in text,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Leveraging hierarchical representations for preserving privacy and utility in text,

Reference 11

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Observation c648c490-fa66-4b47-846c-ab98ee706c71 · outbound

This paper cites Differential privacy in natural language processing the story so far,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Differential privacy in natural language processing the story so far,

Reference 12

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Observation 796a35df-d708-48f7-bda4-bb0f1d69be17 · outbound

This paper cites Group privacy for personalized federated learning,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Group privacy for personalized federated learning,

Reference 13

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Observation 4b522dc4-f394-41e9-bf40-6830e7308eeb · outbound

This paper cites Ad- vancing personalized federated learning: Group privacy, fairness, and beyond,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Ad- vancing personalized federated learning: Group privacy, fairness, and beyond,

Reference 14

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Observation 6b6780b9-fa54-43ad-ac62-61887074acac · outbound

This paper cites Directional privacy for deep learning,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Directional privacy for deep learning,

Reference 15

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Observation 8b3a7cd0-8cb1-4726-8428-dd1fc7d4ba7d · outbound

This paper cites Differential privacy for directional data,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Differential privacy for directional data,

Reference 16

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Observation 1d581313-f13f-408b-b2ca-16ef441d2db7 · outbound

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

Comparing privacy notions for protection against reconstruction attacks in machine learning Exploiting unintended feature leakage in collaborative learning,

Reference 17

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Observation da39afe7-aea0-47ec-9204-0448c55eb3cf · outbound

This paper cites Reconstructing training data with informed adversaries,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Reconstructing training data with informed adversaries,

Reference 18

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Observation 4fde2dc8-d174-4d77-8ee4-0de8fbce9a76 · outbound

This paper cites R ´enyi differential privacy,.

Comparing privacy notions for protection against reconstruction attacks in machine learning R ´enyi differential privacy,

Reference 19

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Observation d607ccdc-7cac-4bc0-96ea-63f9e48f3013 · outbound

This paper cites Comparing systems: Max-case refinement orders and application to differential privacy,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Comparing systems: Max-case refinement orders and application to differential privacy,

Reference 20

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Observation cbf67b5d-d8d4-4f56-84d3-a98a71221614 · outbound

This paper cites an unresolved cited work.

Comparing privacy notions for protection against reconstruction attacks in machine learning Unresolved cited work

Reference 21

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Observation 7da5306d-9d02-4481-b39a-7031b789009f · outbound

This paper cites R ´enyi differential privacy,.

Comparing privacy notions for protection against reconstruction attacks in machine learning R ´enyi differential privacy,

Reference 22

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Observation d68e9632-5281-4552-90b3-b8430dad1af2 · outbound

This paper cites R\'enyi Differential Privacy of the Sampled Gaussian Mechanism.

Comparing privacy notions for protection against reconstruction attacks in machine learning R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 23

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Observation c07a0b76-44e1-4706-953a-304ab6c427bf · outbound

This paper cites Poission subsampled r´enyi differential privacy,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Poission subsampled r´enyi differential privacy,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:26.084994Z

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Observation 1bc8e34a-73b1-448b-bb52-45c4d7d9f77d · outbound

This paper cites Privacy amplification by sub- sampling: Tight analyses via couplings and divergences,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Privacy amplification by sub- sampling: Tight analyses via couplings and divergences,

Reference 25

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Observation 28b5cf66-33e9-42ec-8bf8-330752544a1d · outbound

This paper cites Subsampled r ´enyi differential privacy and analytical moments accountant,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Subsampled r ´enyi differential privacy and analytical moments accountant,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:26.061428Z

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Observation bcaaee7a-b6e9-4d7c-8107-02c8f622fc23 · outbound

This paper cites A better bound gives a hundred rounds: Enhanced privacy guarantees via f- divergences,.

Comparing privacy notions for protection against reconstruction attacks in machine learning A better bound gives a hundred rounds: Enhanced privacy guarantees via f- divergences,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:26.047760Z

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Observation 35a12f8f-b4bc-45e5-9db7-7f6c9b75d392 · outbound

This paper cites The discrete gaussian for differential privacy,.

Comparing privacy notions for protection against reconstruction attacks in machine learning The discrete gaussian for differential privacy,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:26.034214Z

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Observation d29b7178-793d-4d75-9ac9-11c64bc0be36 · outbound

This paper cites Evaluating differentially private machine learning in practice,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Evaluating differentially private machine learning in practice,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:26.019889Z

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Observation 38c756df-dd40-4a5d-9651-f40378e43438 · outbound

This paper cites Deep leakage from gradients,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Deep leakage from gradients,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:26.006060Z

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Observation 5e7b6d6c-2e8b-444c-8839-948424f204b4 · outbound

This paper cites iDLG: Improved Deep Leakage from Gradients.

Comparing privacy notions for protection against reconstruction attacks in machine learning iDLG: Improved Deep Leakage from Gradients

Reference 31

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Observation f185c1a6-6527-4956-a2a1-619295f521eb · outbound

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

Comparing privacy notions for protection against reconstruction attacks in machine learning Inverting gradients - how easy is it to break privacy in federated learning?

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.992872Z

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Observation 016a20f3-7f5a-438a-a50a-51aa02a136e9 · outbound

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

Comparing privacy notions for protection against reconstruction attacks in machine learning Evaluating gradient inversion attacks and defenses in federated learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.979406Z

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Observation 9330d47b-edee-4787-b3ad-2c25ba59340a · outbound

This paper cites Learning to invert: Simple adaptive attacks for gradient inversion in federated learning,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Learning to invert: Simple adaptive attacks for gradient inversion in federated learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.964977Z

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Observation 65b45036-4e96-4e05-b20f-65a36eeb1d99 · outbound

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

Comparing privacy notions for protection against reconstruction attacks in machine learning Reconstructing training data from model gradient, provably,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.806255Z

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Observation f95ce94d-bb60-4d60-8b64-ea41a4054c30 · outbound

This paper cites Sok: Gradient leakage in federated learning,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Sok: Gradient leakage in federated learning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.645337Z

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Observation a4f5326a-ba64-4345-860a-31c0eadad03a · outbound

This paper cites On the Foundations of Quantitative Information Flow,.

Comparing privacy notions for protection against reconstruction attacks in machine learning On the Foundations of Quantitative Information Flow,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.559642Z

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Observation 0e379a86-f145-48fe-b759-7387243e548f · outbound

This paper cites An operational approach to information leakage,.

Comparing privacy notions for protection against reconstruction attacks in machine learning An operational approach to information leakage,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.497696Z

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Observation d5257238-0d9c-4504-a9a3-05a19a45f790 · outbound

This paper cites Information radius,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Information radius,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.484659Z

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Observation 3a83f1ff-a0dd-47ed-921b-47e137bafee3 · outbound

This paper cites Explaining epsilon in local differential privacy through the lens of quantitative information flow.

Comparing privacy notions for protection against reconstruction attacks in machine learning Explaining epsilon in local differential privacy through the lens of quantitative information flow

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-08T23:53:24.818181Z

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Observation 7255a543-bbf7-4198-9dc0-597503d00695 · outbound

This paper cites Closed-form bounds for dp-sgd against record-level inference attacks.

Comparing privacy notions for protection against reconstruction attacks in machine learning Closed-form bounds for dp-sgd against record-level inference attacks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.470358Z

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Observation 5b28b7c9-4090-4902-ae9b-896301b3d277 · outbound

This paper cites Bounding training data reconstruction in private (deep) learning,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Bounding training data reconstruction in private (deep) learning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.455331Z

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Observation 957b5990-6fb6-46f0-9bfa-322e36fde14f · outbound

This paper cites Bounding training data reconstruction in DP-SGD,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Bounding training data reconstruction in DP-SGD,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.441544Z

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Observation de223f3c-3429-4052-bd97-1740e3e9b87a · outbound

This paper cites Calibrating noise to sensitivity in private data analysis,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Calibrating noise to sensitivity in private data analysis,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-08T23:53:24.480421Z

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Observation 1c970636-c48f-4229-a57e-0ace27157af9 · outbound

This paper cites von Mises-Fisher distributions and their statistical divergence.

Comparing privacy notions for protection against reconstruction attacks in machine learning von Mises-Fisher distributions and their statistical divergence

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-08T23:53:24.774431Z

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Observation bf50fde0-4a99-48f7-afce-01166acdab07 · outbound

This paper cites The composition theorem for differential privacy,.

Comparing privacy notions for protection against reconstruction attacks in machine learning The composition theorem for differential privacy,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-08T23:53:24.489499Z

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Observation c536e60e-47fc-474f-b20e-2b2d71b29cd9 · outbound

This paper cites Bayes' capacity as a measure for reconstruction attacks in federated learning.

Comparing privacy notions for protection against reconstruction attacks in machine learning Bayes' capacity as a measure for reconstruction attacks in federated learning

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-08T23:53:24.700326Z

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Observation 4564cd27-cff6-4e0d-8f96-d2ccbb273c9d · outbound

This paper cites See through gradients: Image batch recovery via gradinversion,.

Comparing privacy notions for protection against reconstruction attacks in machine learning See through gradients: Image batch recovery via gradinversion,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.408031Z

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Observation bf3e85c6-fef7-4091-84da-ee579bb66866 · outbound

This paper cites Mea- suring information leakage using generalized gain functions,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Mea- suring information leakage using generalized gain functions,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:53:25.392424Z

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Observation 47615073-3c6e-4e9f-937a-6d9e6eb80ca0 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research,.

Comparing privacy notions for protection against reconstruction attacks in machine learning The mnist database of handwritten digit images for machine learning research,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-08T23:53:24.506094Z

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Observation 3049c233-00ce-442b-a99a-fc1d3dde9c14 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Comparing privacy notions for protection against reconstruction attacks in machine learning Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-08T23:53:24.509975Z

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Observation 4f0c9b1b-fd62-4b3b-ae0f-2370c50b9bea · outbound

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

Comparing privacy notions for protection against reconstruction attacks in machine learning Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-08T23:53:24.514234Z

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Observation 905e0961-4410-464e-8999-03ebecb332d0 · outbound

This paper cites Image quality assess- ment: from error visibility to structural similarity,.

Comparing privacy notions for protection against reconstruction attacks in machine learning Image quality assess- ment: from error visibility to structural similarity,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T23:53:24.518425Z

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Observation c5313740-3b8d-40c3-8235-b4e401483cf7 · outbound

This paper cites Privacy assessment on reconstructed images: Are existing evaluation metrics faithful to human perception?.

Comparing privacy notions for protection against reconstruction attacks in machine learning Privacy assessment on reconstructed images: Are existing evaluation metrics faithful to human perception?

Reference 54

Resolution: malformed identifier; raw_fallback, observed 2026-08-08T23:53:25.360384Z

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