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Paper Citation Record · LEDGER

Inference Privacy: Properties and Mechanisms

As of 16 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

A source-named dated measurement, never combined with another source.

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=sha256:8dcfdcaa904069b30467dbdbf63c6d957c799fee8f8f4f4bfc297f5088fb004e

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:b8227e6fafa03f9000150f89e0c9f3e4f47fe823a7fb209bcba9800b1cd40f69

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:e73e9eb63b2b22e8ceced8d92df88ded1cd1d093ee9fa162e81ba9aee77ca287

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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source=pdf_text observed=2026-08-12T11:04:07.810636Z digest=sha256:3c0f2b4e9da5f90c57a1fa5cd1df8f2c0caadc2aa844df037d80f8a097ee71fb

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=sha256:1cb8c990938789eef9d0f2fe90f7056fcdf7551c6f969a8cac2090e98a4c8006

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=sha256:9d8798224c0270795b9455b6169d953848088cd03103d628b6209dfdde9fd15b

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=sha256:42b4ac576d85ed72f109bdce09fb67ab59e6f0711f49377fe1e453bfdce75d34

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=sha256:95807ba0d12f8387fef0058e8843190ae5977778afe0c87374d81173954d74c7

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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source=pdf_text observed=2026-08-12T11:04:07.837356Z digest=sha256:42dc5cd0807d27726f71621eb734333b524f59da73eff12e908a84d6a7332814

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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source=pdf_text observed=2026-08-12T11:04:07.842268Z digest=sha256:ead010c3167f4edf3e21f3f8822db7b83111f570638136d81f374d4a91fc8768

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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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=sha256:5c76c40ce6127adf3f3afdb17da9cb8bbc40a6dbab0fbd3645683247a27a17ac

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=sha256:8829c6452c9689803aadc1c3dbc0da54829737a1bb59cef9f619aa432455cf4e

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:d66664fbf774eed1a84115ae38722b5d9afbd330c9af4d4471e41ab6e6ee5e95

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=sha256:224c21d99dd903de7f75cc3f75154fec19d5fd60d8d5800206e74b237e3f6319

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=sha256:71a1af3a7e923ce2f40546f0651bd6622b9169956a3458d985d6e2e4d915da12

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=sha256:9b4438996bc5a9f884543c7e7e1a1de0ebd5f19f1826684099130949e2ff222b

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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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:bb889a802532fc954d0ecc16b87aeed2800be99a14078d9144033a551bfd1038

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:eb99e752d7695a4dc7493b5812b7eb1de314831942cc86f837b853c1c1919974

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=sha256:2dfc46f54a1734e6a13ff06325d398dfd6096fc7b8351faf9f24edf88f08373c

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=sha256:26964fb8ae3b35ff0b120434c0a58d96a080ca6de4a80aac9c5c2da4392ff411

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=sha256:8ed3095961a651376fc5ff4a028c6877d14ff424b7bacfe39244d6fe8fde89fb

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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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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source=pdf_text observed=2026-08-12T11:04:07.912595Z digest=sha256:e9ecd4011a8d62857e2f2857f46dbc8bbdd0152eb8d23dd9cada975813af2f31

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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source=pdf_text observed=2026-08-12T11:04:07.916608Z digest=sha256:101dec30b2d52534689dab12a505342b058f98713ed337a47e2d438fb26c5194

Pith citing papers

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