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Paper Citation Record · LEDGER

Inference Privacy: Properties and Mechanisms

As of 15 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2411.18746.

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

pith.paper-citation-record.v1
2411.18746 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:04:07.916608Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

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Source: cited_works

Reference resolution

30 of 30 outbound references displayed

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  • verified fuzzy14
  • unresolved12
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation 20b207b4-2fc5-46e3-aca9-51316dd8b20a · outbound

This paper cites A survey of privacy attacks in machine learning,.

Inference Privacy: Properties and Mechanisms A survey of privacy attacks in machine learning,

Reference 1

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Observation c69ecf44-a55e-4ac3-9274-fa5fe2c83c7b · outbound

This paper cites The algorithmic foundations of differential privacy,.

Inference Privacy: Properties and Mechanisms The algorithmic foundations of differential privacy,

Reference 2

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source=pdf_text observed=2026-08-12T11:04:07.793609Z digest=sha256:419f08f658f7fa150867717a801b49f5cfc26dbd61a6c0465fe2b97cfa32152d

Observation 79813d95-5e4c-4221-9133-9aad5ab29b2a · outbound

This paper cites Deep learning with differential privacy,.

Inference Privacy: Properties and Mechanisms Deep learning with differential privacy,

Reference 3

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source=pdf_text observed=2026-08-12T11:04:07.797966Z digest=sha256:74159bbed4e4c20f9b820a48f8def4744905553ec4f8da602e80dce3a6fbd6ee

Observation d5190238-f562-4d98-b1ba-52745b74fa4e · outbound

This paper cites Deep learning with label differential privacy,.

Inference Privacy: Properties and Mechanisms Deep learning with label differential privacy,

Reference 4

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Observation c7934cb1-5ecb-4ffa-af56-4aef4760258e · outbound

This paper cites Survey: Leakage and privacy at inference time,.

Inference Privacy: Properties and Mechanisms Survey: Leakage and privacy at inference time,

Reference 5

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source=pdf_text observed=2026-08-12T11:04:07.806699Z digest=sha256:229f80accf2fe5b57fe9d3eff71f1da95d2df1b0e77bc8f4a2d285087acbdb9d

Observation ec7d1b32-a975-4b97-95bf-0e4da0275dff · outbound

This paper cites Managing your private and public data: Bringing down inference attacks against your privacy,.

Inference Privacy: Properties and Mechanisms Managing your private and public data: Bringing down inference attacks against your privacy,

Reference 6

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Observation 9bca1ad8-b825-4404-9f73-f8e20f7f5edd · outbound

This paper cites Vicious Classifiers: Assessing Inference-time Data Reconstruction Risk in Edge Computing.

Inference Privacy: Properties and Mechanisms Vicious Classifiers: Assessing Inference-time Data Reconstruction Risk in Edge Computing

Reference 7

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source=pdf_text observed=2026-08-12T11:04:07.814852Z digest=sha256:c193bbc25f7e1d6d2dd17678f6c59de4586cd969127d07b0bd55b04e1c5b50e5

Observation 3f62b9f3-50ed-4331-8848-b4310ee61ef8 · outbound

This paper cites Local privacy and statistical minimax rates,.

Inference Privacy: Properties and Mechanisms Local privacy and statistical minimax rates,

Reference 8

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source=pdf_text observed=2026-08-12T11:04:07.819294Z digest=sha256:768a36cfbb117556e5f9dcd74cf1f8ae88348215f64fb03a56a70f69443500dd

Observation 1c97deef-4293-4269-a3bf-3d427fbbd2d4 · outbound

This paper cites What can we learn privately?.

Inference Privacy: Properties and Mechanisms What can we learn privately?

Reference 9

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source=pdf_text observed=2026-08-12T11:04:07.823207Z digest=sha256:208bc19aa3f354552ca99cab741d87a5ded0ce5e4a00dabb049af11b868480b7

Observation fee38d5a-5fce-4c97-a12d-1642a5e52057 · outbound

This paper cites Privacy-preserving Machine Learning through Data Obfuscation.

Inference Privacy: Properties and Mechanisms Privacy-preserving Machine Learning through Data Obfuscation

Reference 10

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source=pdf_text observed=2026-08-12T11:04:07.827141Z digest=sha256:f0a60446473d78b9c4a41c90841337366da2a11280a9748615bffa56b396d884

Observation 33cc87ce-db6b-4ec8-9f79-1f0417b35a23 · outbound

This paper cites Data sanitization for privacy preservation on social network,.

Inference Privacy: Properties and Mechanisms Data sanitization for privacy preservation on social network,

Reference 11

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Observation e4d41903-e61d-4415-90a3-f9953ef9d4b2 · outbound

This paper cites Certified Data Removal from Machine Learning Models.

Inference Privacy: Properties and Mechanisms Certified Data Removal from Machine Learning Models

Reference 12

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Observation 2ee094ed-1ec0-4645-9c01-fbb29b420b84 · outbound

This paper cites Distillation as a defense to adversarial perturbations against deep neural networks,.

Inference Privacy: Properties and Mechanisms Distillation as a defense to adversarial perturbations against deep neural networks,

Reference 13

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Observation 6a009f71-8ad7-4beb-8678-2f12e178db9e · outbound

This paper cites Eternal sunshine of the spotless net: Selective forgetting in deep networks,.

Inference Privacy: Properties and Mechanisms Eternal sunshine of the spotless net: Selective forgetting in deep networks,

Reference 14

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Observation 6795c222-57f5-4e98-a14e-e775b0a63de2 · outbound

This paper cites Word2vec,.

Inference Privacy: Properties and Mechanisms Word2vec,

Reference 15

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source=pdf_text observed=2026-08-12T11:04:07.850545Z digest=sha256:1dc993038bc8f9aa3a019201226be7a5044469ae46e073da785d15dafefeec47

Observation b1116205-4d59-41b1-963d-d06c8b12ea7b · outbound

This paper cites A normalized levenshtein distance metric,.

Inference Privacy: Properties and Mechanisms A normalized levenshtein distance metric,

Reference 16

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source=pdf_text observed=2026-08-12T11:04:07.854678Z digest=sha256:212f2040154379eabd84c432fbca30e6f1dfdf05308b83e6a0b544decdd6ed3c

Observation 1e39a8ee-c1cf-4a95-a328-86cd192a10bf · outbound

This paper cites Local differential privacy on metric spaces: optimizing the trade-off with utility,.

Inference Privacy: Properties and Mechanisms Local differential privacy on metric spaces: optimizing the trade-off with utility,

Reference 17

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source=pdf_text observed=2026-08-12T11:04:07.858889Z digest=sha256:d0d04fe8634720655f92b9ff3d822a4f24cf520809bf2b69c03c24198263767b

Observation 4ebd9d9e-0017-4f74-b319-d1869841094a · outbound

This paper cites Metric Differential Privacy at the User-Level Via the Earth Mover's Distance.

Inference Privacy: Properties and Mechanisms Metric Differential Privacy at the User-Level Via the Earth Mover's Distance

Reference 18

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local_arxiv, observed 2026-08-12T11:04:08.007072Z

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source=pdf_text observed=2026-08-12T11:04:07.863356Z digest=sha256:af3ad7b0df25ee246a524856927d9db465f88692f75b4207358a7a2bfa115a22

Observation 72ea6ba0-81cf-428b-af46-14d1874f9359 · outbound

This paper cites Lipschitz regularity of deep neural networks: analysis and efficient estimation,.

Inference Privacy: Properties and Mechanisms Lipschitz regularity of deep neural networks: analysis and efficient estimation,

Reference 19

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source=pdf_text observed=2026-08-12T11:04:07.867909Z digest=sha256:bacef9c6307ea95481b9bd0c3b4bfd80ced189fbf777a2afa52ed40c7fd4386a

Observation f59f8dc5-166d-45c6-ac80-602f8e370236 · outbound

This paper cites Efficient and accurate estimation of lipschitz constants for deep neural networks,.

Inference Privacy: Properties and Mechanisms Efficient and accurate estimation of lipschitz constants for deep neural networks,

Reference 20

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source=pdf_text observed=2026-08-12T11:04:07.872057Z digest=sha256:27c5d60577680a92c621f9346e15ace1578abd58f6308f112d60f605942cc265

Observation a74c43fa-bd85-4f8c-ad7a-3f3de546ec9e · outbound

This paper cites A Unified Algebraic Perspective on Lipschitz Neural Networks.

Inference Privacy: Properties and Mechanisms A Unified Algebraic Perspective on Lipschitz Neural Networks

Reference 21

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source=pdf_text observed=2026-08-12T11:04:07.875868Z digest=sha256:393286b162c39fe08540d1087c6a305c05aaefd88bcf86a3aa2e138bdf55684e

Observation ebdf0bc0-18a5-4d5c-89b6-190e8358f38f · outbound

This paper cites Orthogonalizing Convolutional Layers with the Cayley Transform.

Inference Privacy: Properties and Mechanisms Orthogonalizing Convolutional Layers with the Cayley Transform

Reference 22

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source=pdf_text observed=2026-08-12T11:04:07.880234Z digest=sha256:0b99cdd24f8da86ea0f7bdb902324acb7d0eb9a7fbcb558fa9efeaaf2940b857

Observation 58430a4e-4dc5-411d-8458-c78629d2b926 · outbound

This paper cites Lot: Layer-wise orthogonal training on improving l2 certified robustness,.

Inference Privacy: Properties and Mechanisms Lot: Layer-wise orthogonal training on improving l2 certified robustness,

Reference 23

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source=pdf_text observed=2026-08-12T11:04:07.884849Z digest=sha256:fe20fb3754e7982423c0ca774c9c52329e55b918e7f2236c5ad230044b88106c

Observation 717a329f-d50b-4e6d-a81a-1299e649b87f · outbound

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

Inference Privacy: Properties and Mechanisms Learning multiple layers of features from tiny images,

Reference 24

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source=pdf_text observed=2026-08-12T11:04:07.888871Z digest=sha256:c63ed29f649487eea3aaa9da08b7e4001ff568e46c295a4b6a780d949045c18f

Observation e3e5cdfe-aa12-4880-93df-c715781f1791 · outbound

This paper cites Pytorch image models,.

Inference Privacy: Properties and Mechanisms Pytorch image models,

Reference 25

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source=pdf_text observed=2026-08-12T11:04:07.893218Z digest=sha256:cacbe033e112007e26d37d71837bff7f55869e554ddfa230acb1ecdc6d41a9d7

Observation 91586cb3-a56e-4638-b63b-f9e718313bff · outbound

This paper cites Direct parameterization of lipschitz- bounded deep networks,.

Inference Privacy: Properties and Mechanisms Direct parameterization of lipschitz- bounded deep networks,

Reference 26

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source=pdf_text observed=2026-08-12T11:04:07.899432Z digest=sha256:9426b3a2a48fda3c3f8565bc982b3f3aeed3daee94f1230757cfebab49195a6f

Observation 4e20244e-c078-4d0b-881b-e3824cab26fa · outbound

This paper cites SPLITZ: Certifiable Robustness via Split Lipschitz Randomized Smoothing.

Inference Privacy: Properties and Mechanisms SPLITZ: Certifiable Robustness via Split Lipschitz Randomized Smoothing

Reference 27

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local_arxiv, observed 2026-08-12T11:04:07.965156Z

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source=pdf_text observed=2026-08-12T11:04:07.903664Z digest=sha256:a03d26410dc0354f75731d209ec4c2d952aee39f2a2d75d0d8c34c5625bd768e

Observation e606f139-7e55-4f65-aca7-f2ea2d4ca3f0 · outbound

This paper cites Busemann, The geometry of geodesics.

Inference Privacy: Properties and Mechanisms Busemann, The geometry of geodesics

Reference 28

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source=pdf_text observed=2026-08-12T11:04:07.908181Z digest=sha256:84f2a15644769811c7594e90e858e392e5273ce839e1c6beda874018ccf88df4

Observation c32437f1-9c6e-4777-9244-0ea7eb726ff3 · outbound

This paper cites (47) Notice that: Z Z2∈S′ 2 fz(Z2)dz = Z Z2|ln fz (Z2 ) fz (Z2 −(C(xb )−C(xa )) >ϵ fu(Z2)dz.

Inference Privacy: Properties and Mechanisms (47) Notice that: Z Z2∈S′ 2 fz(Z2)dz = Z Z2|ln fz (Z2 ) fz (Z2 −(C(xb )−C(xa )) >ϵ fu(Z2)dz

Reference 29

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Observation 3abdf3c4-6665-4116-ad47-c49fe244a5de · outbound

This paper cites (64) Notice that: Z Z3∈S′ 2 fz(Z3)dz = Z Z3|ln fz (Z3 ) fz (Z3 −(xb −xa )) >ϵ fz(Z3)dz.

Inference Privacy: Properties and Mechanisms (64) Notice that: Z Z3∈S′ 2 fz(Z3)dz = Z Z3|ln fz (Z3 ) fz (Z3 −(xb −xa )) >ϵ fz(Z3)dz

Reference 30

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Pith citing papers

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