Paper Citation Record · LEDGER

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise

As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2509.10385.

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

pith.paper-citation-record.v1
2509.10385 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:03:14.954752Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

33 of 33 outbound references displayed

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verified fuzzy0
unresolved33
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b7182422-e396-458f-a05b-b3aadd35ca87 · outbound

This paper cites Membership inference attacks against machine learning models,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Membership inference attacks against machine learning models,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:11.149164Z

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Observation 36fa8418-6d80-40e3-bfc9-962a3279b49f · outbound

This paper cites Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:11.294749Z

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Observation 5656f08c-2335-434b-b320-43ebe6ddc5f2 · outbound

This paper cites The secret sharer: Evaluating and testing unintended memorization in neural networks,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise The secret sharer: Evaluating and testing unintended memorization in neural networks,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:11.401308Z

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Observation d1d30cbc-a74e-4545-b74f-b7ec707d42a4 · outbound

This paper cites Property inference attacks on fully connected neural networks using permutation invariant representations,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Property inference attacks on fully connected neural networks using permutation invariant representations,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:11.514744Z

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Observation 838fd683-7e44-46df-9a4a-565cbb2d2980 · outbound

This paper cites Federated learning in non-iid settings aided by differentially private synthetic data,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Federated learning in non-iid settings aided by differentially private synthetic data,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:11.604750Z

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Observation f765c905-0ace-48c9-af33-8b442bccf764 · outbound

This paper cites Gen- erative models for effective ml on private, decentralized datasets,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Gen- erative models for effective ml on private, decentralized datasets,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:11.730986Z

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Observation b6f5e535-0544-4295-81f1-5dfb9db2ef4c · outbound

This paper cites Federated learning: Challenges, methods, and future directions,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Federated learning: Challenges, methods, and future directions,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:11.859623Z

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Observation 7bd66ecc-638f-43e8-98b8-768a8212802d · outbound

This paper cites Federated machine learning: Concept and applications,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Federated machine learning: Concept and applications,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:11.994752Z

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Observation 419b763c-1c08-4ad0-82a3-a9a12a9ac5df · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Communication-efficient learning of deep networks from decentralized data,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.046594Z

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Observation 49123223-5cff-4827-accf-2b88fe127b80 · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Federated Learning: Strategies for Improving Communication Efficiency

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.095529Z

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Observation 95044c43-a191-48dc-921a-95541548519f · outbound

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

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Inverting gradients-how easy is it to break privacy in federated learning?,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.164358Z

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Observation 5b43cb27-b7a7-4bfa-a2cd-b5ef5d9f0b42 · outbound

This paper cites Handling privacy-sensitive medical data with federated learning: challenges and future directions,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Handling privacy-sensitive medical data with federated learning: challenges and future directions,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.234756Z

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Observation d349c528-6c18-45a7-a935-84bac8eac6e2 · outbound

This paper cites Cafe: Catastrophic data leakage in vertical federated learning,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Cafe: Catastrophic data leakage in vertical federated learning,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.304754Z

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Observation d9bf468b-0406-4b11-9789-0896f29e744b · outbound

This paper cites Tapfed: Threshold secure aggregation for privacy-preserving federated learning,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Tapfed: Threshold secure aggregation for privacy-preserving federated learning,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.384756Z

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Observation 8054d727-8d54-4749-93f2-1b3398a6d3d5 · outbound

This paper cites Privacy-preserving federated learning via functional encryption, revisited,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Privacy-preserving federated learning via functional encryption, revisited,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.494759Z

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Observation 4f6b6365-5f26-4b0d-b474-b45f2745b628 · outbound

This paper cites Differentially private federated learning: An information- theoretic perspective,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Differentially private federated learning: An information- theoretic perspective,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.564738Z

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Observation 8b853793-cbf8-48bb-9895-5392edcfdeea · outbound

This paper cites Fedv: Privacy-preserving federated learning over vertically partitioned data,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Fedv: Privacy-preserving federated learning over vertically partitioned data,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.634859Z

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Observation f9ce7cee-ec46-4ef4-9be3-ed9f1fd36de9 · outbound

This paper cites Differentially Private Federated Learning: A Client Level Perspective.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Differentially Private Federated Learning: A Client Level Perspective

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.740476Z

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Observation d3fad9e8-e42f-4d9a-a3d6-de774b2d67e2 · outbound

This paper cites DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixing.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixing

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.884752Z

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Observation 4e252b6c-45bf-4633-be0c-6ac7a2fa5703 · outbound

This paper cites A correlated noise-assisted decentralized differentially private estimation protocol, and its application to fmri source separation,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise A correlated noise-assisted decentralized differentially private estimation protocol, and its application to fmri source separation,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:12.971980Z

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Observation 52c89b58-0889-439f-8949-65dc839122f8 · outbound

This paper cites Privacy-preserving non-negative matrix factorization with outliers,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Privacy-preserving non-negative matrix factorization with outliers,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:13.054769Z

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Observation aa588a51-762a-43ba-ac6b-7842acef3dd9 · outbound

This paper cites Approximating functions with approximate privacy for applications in signal estimation and learning,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Approximating functions with approximate privacy for applications in signal estimation and learning,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:13.194838Z

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Observation f60c09ea-732c-4f80-97ad-8286bf5329cb · outbound

This paper cites Subsampled rényi differential privacy and analytical moments accountant,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Subsampled rényi differential privacy and analytical moments accountant,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:13.294750Z

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Observation 5398ecef-5bf9-40ee-8d3d-ed150c339a26 · outbound

This paper cites Gradient-based learning applied to document recognition,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Gradient-based learning applied to document recognition,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:13.484872Z

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Observation 183fde81-7a12-43ab-9734-b549958878bf · outbound

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

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:13.634753Z

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Observation fa3d0941-316a-41fb-876a-8255386dd287 · outbound

This paper cites Dppro: Differentially private high-dimensional data release via random projection,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Dppro: Differentially private high-dimensional data release via random projection,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:13.774755Z

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Observation d34e5839-56bd-432e-86c9-3b400ed54978 · outbound

This paper cites Privacy-preserving data mining,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Privacy-preserving data mining,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:13.954745Z

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Observation d9ed224a-9943-4218-b231-81e3b27f9a16 · outbound

This paper cites Synthesizing differentially private datasets using random mixing,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Synthesizing differentially private datasets using random mixing,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:14.150595Z

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Observation 67a9c27b-1194-4b5b-9d1a-cc61816542fa · outbound

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

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Calibrating noise to sensitivity in private data analysis,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:14.274755Z

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Observation 6d7bc043-b13c-40f7-ba54-6ff007d87d59 · outbound

This paper cites The algorithmic foundations of differential privacy,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise The algorithmic foundations of differential privacy,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:14.624751Z

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Observation a07bd80a-721e-4462-8144-4ab7485f75d6 · outbound

This paper cites Mechanism design via differential privacy,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Mechanism design via differential privacy,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:14.874755Z

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Observation 9cceb0fb-ff7a-4611-b85e-201e9c3f2911 · outbound

This paper cites Rényi differential privacy,.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise Rényi differential privacy,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:14.954752Z

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Observation a8e4046f-7a08-429a-ba9a-7bc46d5dcb59 · outbound

This paper cites 265–284, Springer, 2006.

Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise 265–284, Springer, 2006

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-04T18:03:14.501411Z

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Pith citing papers

No inbound Pith citation observations are available.