Paper Citation Record · LEDGER

R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 22 inbound Pith citation observations for arXiv:1908.10530.

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

pith.paper-citation-record.v1
1908.10530 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 22 of 22 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:53:23.875876Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T22:15:39.238694Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

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

Comparing privacy notions for protection against reconstruction attacks in machine learning cites this paper.

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

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T23:53:23.875876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49f452c2-1770-4c3d-9fdf-62e0c5b6a715 · inbound

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning cites this paper.

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:58:12.413237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08049848-d47f-49ed-b494-84c6d2c07953 · inbound

Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States cites this paper.

Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-19T12:02:16.860826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dff54995-edaf-4f7c-aacf-bff8c026eef5 · inbound

Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It? cites this paper.

Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It? R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T00:52:58.591295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 177ace3e-e7bf-46d1-bff6-4b1d11227e09 · inbound

Achieving Hilbert-Schmidt Independence Under R\'enyi Differential Privacy for Fair and Private Data Generation cites this paper.

Achieving Hilbert-Schmidt Independence Under R\'enyi Differential Privacy for Fair and Private Data Generation R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-05T13:59:45.652141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52ce9d22-308f-41d6-b15a-3801ad6ba553 · inbound

DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling cites this paper.

DPQuant: Efficient and Differentially-Private Model Training via Dynamic Quantization Scheduling R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T19:11:46.605965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ab1300f6-1984-46a8-9ad5-166cbe939d18 · inbound

Tight Privacy Audit in One Run cites this paper.

Tight Privacy Audit in One Run R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T20:29:12.795378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 181e9df7-b50b-4485-b254-eb204d3c396d · inbound

AVEC: Bootstrapping Privacy for Local LLMs cites this paper.

AVEC: Bootstrapping Privacy for Local LLMs R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T20:47:45.718288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8be9ea34-4093-4318-97e0-0ccd6374d407 · inbound

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD cites this paper.

Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-16T13:37:56.490126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b9cec0ff-7d77-40f3-845f-902c87d549a7 · inbound

Differentially Private Model Merging cites this paper.

Differentially Private Model Merging R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:41:05.184565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f553e129-39b0-4b62-9674-d85f786c2f14 · inbound

$\alpha$-Wasserstein Mechanism for R\'{e}nyi Pufferfish Privacy cites this paper.

$\alpha$-Wasserstein Mechanism for R\'{e}nyi Pufferfish Privacy R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:11.318104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 307b0fc7-5ee1-41f6-83f2-a2462a2675ad · inbound

Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds cites this paper.

Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:56:06.110821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 525f8087-0805-4610-b3c3-c8c9c3f63186 · inbound

Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds cites this paper.

Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-06-30T23:35:07.414295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation da1fb008-01a3-459e-a4ca-baa74db8cf58 · inbound

INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy cites this paper.

INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 103

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:05:53.323911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f2deceff-6547-431c-ad62-66f05caae577 · inbound

Rethinking the Security of DP-SGD: A Corrected Analysis of Differentially Private Machine Learning cites this paper.

Rethinking the Security of DP-SGD: A Corrected Analysis of Differentially Private Machine Learning R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-20T18:18:52.482514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2a8c6ac2-b54c-4f62-846e-776a9929e59f · inbound

DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models cites this paper.

DP-SelFT: Differentially Private Selective Fine-Tuning for Large Language Models R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:38:19.409074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cd8963f8-73e2-476a-ba7d-64dcecf23ab3 · inbound

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks cites this paper.

Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networks R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:14:02.113829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cb47bd2b-6835-411f-beba-33001adc67cc · inbound

DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis cites this paper.

DP-SAPF: Saliency-Aware Parameter Fine-tuning of Public Models for Differentially Private Image Synthesis R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T14:33:30.893546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d1b712b9-15ba-400c-a95c-ca1c042fa9b0 · inbound

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models cites this paper.

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 245

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:27:29.609384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4fcc9f17-45ca-4807-a5a2-3594052a69db · inbound

AdaPrivate-TS: Private Thompson Sampling for Contextual Bandits with Privacy Amplification cites this paper.

AdaPrivate-TS: Private Thompson Sampling for Contextual Bandits with Privacy Amplification R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T06:29:37.652311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b7f441c6-2306-4517-b639-549d36894f3b · inbound

Differentially Private Natural Gradient Descent cites this paper.

Differentially Private Natural Gradient Descent R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-07-08T22:15:39.240176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a1e9ad68-0d49-4442-806c-1dcf369b9a1a · inbound

End-to-End Differential Privacy in Training Deep Neural Network Classifiers cites this paper.

End-to-End Differential Privacy in Training Deep Neural Network Classifiers R\'enyi Differential Privacy of the Sampled Gaussian Mechanism

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-01T12:29:20.455712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.