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Paper Citation Record · LEDGER

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches

As of 7 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 1 inbound Pith citation observation for arXiv:2505.24603.

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

pith.paper-citation-record.v1
2505.24603 v2

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:31:57.698155Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-09T16:02:19.749100Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

18 of 18 outbound references displayed

  • verified exact0
  • verified fuzzy13
  • unresolved4
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 0d471baf-75ef-4463-9335-e6227959fc51 · outbound

This paper cites (5) For an invertible matrix A, we have [Brookes, 2020, Section.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches (5) For an invertible matrix A, we have [Brookes, 2020, Section

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:59.187523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:57.014477Z digest=sha256:95bbf32554c4aab098778be71bb1ebaed8c86ebd29d9bec4e4d022d1ce0d9ccb

Observation aa0417dd-a2e7-4a6b-8cc5-f2ac7e099564 · outbound

This paper cites Our second baseline, from Sheffet [2017, Algorithm 1], was implemented according to the description in Appendix H.2.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Our second baseline, from Sheffet [2017, Algorithm 1], was implemented according to the description in Appendix H.2

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:58.514120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:57.457109Z digest=sha256:2f5ae9aefb3520610595f73e2084542488e41c58f5083353377e19ff53dbb433

Observation 734a4c28-4f1b-4778-abab-a328c21d8c5d · outbound

This paper cites 1, Sheffet '17]: k d = 2.500 ADASSP [Wang '18] [Alg.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches 1, Sheffet '17]: k d = 2.500 ADASSP [Wang '18] [Alg

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:58.114719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:57.698155Z digest=sha256:83594d80f50917c6dc8f5f64c49f61a30c5e06aaa295f17aeff4129a4299dd8c

Observation f1c2e91e-c0d0-4faa-8561-4fc6b37984ad · outbound

This paper cites an unresolved cited work.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-07T12:31:59.353603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:56.914296Z digest=sha256:5950f8a23ee67e79cbc575d7396c39b647deee15296a6f9e3064b4de0ca7d76b

Observation 01153b25-38a5-4d03-a0b9-99af0166c681 · outbound

This paper cites We define the discriminant to be ∆H = 1 + 2γ + γ2 log 1 − 1 γ 2 − 8γ 1 + γ + γ2 log 1 − 1 γ which is non-negative.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches We define the discriminant to be ∆H = 1 + 2γ + γ2 log 1 − 1 γ 2 − 8γ 1 + γ + γ2 log 1 − 1 γ which is non-negative

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:59.059272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:57.084978Z digest=sha256:66d2bd11a1f23cec0fd69d10c184e833aacc2b7f558f84d921a04eb44597fb25

Observation 76ec1e86-c8d8-4766-90fa-a9e607b1cb8c · outbound

This paper cites The first case (when γ ≤ τ ) trivially satisfies this.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches The first case (when γ ≤ τ ) trivially satisfies this

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:58.866486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:57.155546Z digest=sha256:4432e0aa25da2470b64348a0ea4d9d682737d8e37f6c60db136a08b23d2bcb0b

Observation 098f79fd-9113-49ed-b9fb-3d3430538d1b · outbound

This paper cites H Algorithms: Linear Regression H.1 AdaSSP Algorithm 3 AdaSSP [Wang, 2018] Input: Dataset (X, Y); Privacy parameters ε, δ; Bounds: max i∈[n] ∥xi∥2 ≤ C2 X , max i∈[n] |yi|2 ≤ C2 Y.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches H Algorithms: Linear Regression H.1 AdaSSP Algorithm 3 AdaSSP [Wang, 2018] Input: Dataset (X, Y); Privacy parameters ε, δ; Bounds: max i∈[n] ∥xi∥2 ≤ C2 X , max i∈[n] |yi|2 ≤ C2 Y

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:58.747142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:57.273530Z digest=sha256:73d7f0219f58774e6f61e285fa64fce9c17793e4c11b00fb41eaeced9ddf58b4

Observation 047cfbc4-6e81-4aa8-b0bf-e421a056c134 · outbound

This paper cites 1: Compute λmin := λmin((X, Y)⊤(X, Y)).

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches 1: Compute λmin := λmin((X, Y)⊤(X, Y))

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:58.672901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:57.367675Z digest=sha256:4a8c458671141172623d1183a9f325f02e1a469b4c0b89f31b95dcc0b994783d

Observation b879662d-2343-41e1-bc28-aef2b1ef8a87 · outbound

This paper cites The train and test loaders were generated using torch.utils.data.DataLoader with shuffling enabled.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches The train and test loaders were generated using torch.utils.data.DataLoader with shuffling enabled

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:58.394176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:57.557315Z digest=sha256:5c3f2227b7231434c6d264bbbec5091db10bc394b9391b479bf51077ed705ebe

Observation dda57975-7027-48d7-aed0-089dde8aaaa5 · outbound

This paper cites Runtime comparisons show the ratio of execution times for the largest simulated ε.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Runtime comparisons show the ratio of execution times for the largest simulated ε

Reference 500

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:58.282853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:57.611960Z digest=sha256:f73416139885364780f9fbf483bda163fb95332fcc8c6230b0d709caa7d133f4

Observation 206ff287-f1af-41ae-9f71-2a3bd60e640c · outbound

This paper cites Improved approximation algorithms for large matrices via random projections.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Improved approximation algorithms for large matrices via random projections

Reference 1961

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:59.610056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:56.343762Z digest=sha256:13ba04220b723048b31d34f326573ff1fc88a5b5234f9cdbfca45bec2f075cf0

Observation 18eaef21-b582-429c-bc96-185300c1478f · outbound

This paper cites Private Approximations of the 2nd-Moment Matrix Using Existing Techniques in Linear Regression.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Private Approximations of the 2nd-Moment Matrix Using Existing Techniques in Linear Regression

Reference 2006

Resolution
unresolved
no resolver link, observed 2026-08-07T12:31:56.571254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:31:56.571254Z digest=sha256:311fe292150ea50ea1ef9050c776065f4ea2820146819117c122ece9fb799149

Observation b2d81ec5-8e9d-4036-bfe9-4a0c9089839c · outbound

This paper cites edu/~kriz/learning-features-2009-TR.pdf.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches edu/~kriz/learning-features-2009-TR.pdf

Reference 2009

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:59.718160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:56.257263Z digest=sha256:4dcc452300c26f1d8fc91a43dd3b264e31b9d3d8ae8963c0be44fecce75b10a1

Observation ca16415d-1375-40b5-8f97-5f153adf7dfa · outbound

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

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 2014

Resolution
unresolved
no resolver link, observed 2026-08-07T12:31:56.747036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:31:56.747036Z digest=sha256:8bb677113b7ad9fc4f76363fdfa56129bfe9ee4bfd1dbc2cacdc399eea1aae1d

Observation 8a782b08-c043-4001-a549-aa508a3e5f12 · outbound

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

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Opacus: User-Friendly Differential Privacy Library in PyTorch

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-07T12:31:56.839268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:31:56.839268Z digest=sha256:2295060600d47e0caec402ade285035b3f57e37f6bc2bd4b53a3f5e676569a83

Observation 4a5d52e0-188e-418d-9a3e-135870c17f81 · outbound

This paper cites A History of Census Privacy Protections.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches A History of Census Privacy Protections

Reference 2018

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:59.821538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:56.096909Z digest=sha256:ebd21dfb230e56d4b961cbea902f105b3e197430e4a337fdc3d5c8842a95db2e

Observation 0bf8d54e-483c-48f4-9483-3ee7ab31b65a · outbound

This paper cites Private Regression via Data-Dependent Sufficient Statistic Perturbation.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Private Regression via Data-Dependent Sufficient Statistic Perturbation

Reference 2020

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T12:31:57.979546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:56.163962Z digest=sha256:4a5bc88afd23189f55717c66939b10da63a4fc01dc07dc63b36a0988f2b2af1a

Observation bac6eb0c-13f5-44d5-8ded-60858f493e67 · outbound

This paper cites Yuchang Sun, Jiawei Shao, Songze Li, Yuyi Mao, and Jun Zhang.

The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches Yuchang Sun, Jiawei Shao, Songze Li, Yuyi Mao, and Jun Zhang

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:31:59.457278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:31:56.653847Z digest=sha256:588936c6192fbf051741c11a8345b4c5409e48fd37fb46731352a4f58126ae67

Pith citing papers

Observation c0c7d6b5-cd30-40f3-9191-9c775c7045fa · inbound

Quadratic Objective Perturbation: Curvature-Based Differential Privacy cites this paper.

Quadratic Objective Perturbation: Curvature-Based Differential Privacy The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-09T21:58:47.171884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-09T16:02:19.749100Z digest=sha256:43052234c179d2d74f48bfe40eefc454cee2fb9f7f8d29e0ff591198ba5c920a