Paper Citation Record · LEDGER

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach

As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2412.10612.

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

pith.paper-citation-record.v1
2412.10612 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:54:59.582239Z

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

verified exact1
verified fuzzy22
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a12d55f9-6b3f-43a8-8bba-cf0f54584fd6 · outbound

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

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Calibrating noise to sensitivity in private data analysis,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:55:00.295797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5549bbb4-d1b1-47e8-be0c-40bc5850870f · outbound

This paper cites Accuracy first: Selecting a differential privacy level for accuracy constrained erm,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Accuracy first: Selecting a differential privacy level for accuracy constrained erm,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:55:00.274797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 152d47b2-bbb8-4152-9512-30e8901026b7 · outbound

This paper cites Brownian noise reduction: Maximizing privacy subject to accuracy constraints,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Brownian noise reduction: Maximizing privacy subject to accuracy constraints,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:55:00.104033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aca0844f-aac7-43a1-ba88-493962c741cc · outbound

This paper cites Ireduct: Differential privacy with reduced relative errors,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Ireduct: Differential privacy with reduced relative errors,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:55:00.087416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a74551c1-021a-411d-b170-3b6a9af9aa58 · outbound

This paper cites Privacy and Utility Tradeoff in Approximate Differential Privacy.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Privacy and Utility Tradeoff in Approximate Differential Privacy

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T15:54:59.446537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84d2157a-953b-4771-baa5-672bf6081928 · outbound

This paper cites Differential privacy via a truncated and normalized laplace mechanism,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Differential privacy via a truncated and normalized laplace mechanism,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:55:00.069569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 904566be-177a-46f0-b469-4055b1c3a37e · outbound

This paper cites The Bounded Laplace Mechanism in Differential Privacy.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach The Bounded Laplace Mechanism in Differential Privacy

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T15:54:59.456736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 536baafc-706e-4c6d-a150-f73a82d6f90a · outbound

This paper cites Canonical noise distributions and private hypothesis tests,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Canonical noise distributions and private hypothesis tests,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:55:00.052301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c53db5a7-2d31-4d15-ac92-69368e5c062c · outbound

This paper cites Gaussian Differential Privacy,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Gaussian Differential Privacy,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T15:54:59.466310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b860dc35-bef9-4ab8-96a1-828206e0337a · outbound

This paper cites Log-concave and multivariate canonical noise distributions for differential privacy,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Log-concave and multivariate canonical noise distributions for differential privacy,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:55:00.034791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2a1dfa74-3ab4-45a7-b7e9-b5ecab4dc067 · outbound

This paper cites The optimal noise-adding mechanism in differential privacy,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach The optimal noise-adding mechanism in differential privacy,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:55:00.004385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 11df6dfb-6e89-4c9b-bc82-327e1dbb4b5e · outbound

This paper cites The staircase mech- anism in differential privacy,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach The staircase mech- anism in differential privacy,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.988066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 459638a7-f5f1-4afb-b303-babe049591c2 · outbound

This paper cites Optimal data-independent noise for differential privacy,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Optimal data-independent noise for differential privacy,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.972497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5872fcda-98fe-46db-b763-392d2eb28a0c · outbound

This paper cites Budget Recycling Differential Privacy.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Budget Recycling Differential Privacy

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:54:59.701933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f3950a4d-c6c3-4ae6-bec6-22622c4f7c12 · outbound

This paper cites Smooth sensitivity and sampling in private data analysis,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Smooth sensitivity and sampling in private data analysis,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.956703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e9a8ea6c-1953-43d4-8505-451e062c96b6 · outbound

This paper cites Our data, ourselves: Privacy via distributed noise generation,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Our data, ourselves: Privacy via distributed noise generation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.938530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cad205a9-69d4-449e-a7a9-430e95026363 · outbound

This paper cites Boosting and differential privacy,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Boosting and differential privacy,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.922601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c9bae3ab-3ea9-4dd6-8d1a-4a316cebd7fb · outbound

This paper cites The composition theorem for differential privacy,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach The composition theorem for differential privacy,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.907499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7780ae19-7eeb-4c36-8987-44162581d9e2 · outbound

This paper cites The complexity of computing the opti- mal composition of differential privacy,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach The complexity of computing the opti- mal composition of differential privacy,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.891768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aaee873d-4b6d-47ca-8e3a-2b648daf8279 · outbound

This paper cites R ´enyi differential privacy,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach R ´enyi differential privacy,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.875050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d1760e97-ef84-4d1c-89bf-d9f26e9a2bcf · outbound

This paper cites Concentrated Differential Privacy.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Concentrated Differential Privacy

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T15:54:59.524340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f73dfd9-f22e-481e-92fb-f8e197aaae48 · outbound

This paper cites Concentrated differential privacy: Simplifi- cations, extensions, and lower bounds,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Concentrated differential privacy: Simplifi- cations, extensions, and lower bounds,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T15:54:59.529361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5c9a071-da09-4cc3-9957-ed3b6e384157 · outbound

This paper cites Privacy loss classes: The central limit theorem in differential privacy,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Privacy loss classes: The central limit theorem in differential privacy,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.850253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ea9710f6-9f5a-4c19-956a-18a242ca7ef4 · outbound

This paper cites Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T15:54:59.538675Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4339bf8c-e72f-4292-b09c-ffaa52d917a8 · outbound

This paper cites Computing tight differential privacy guarantees using fft,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Computing tight differential privacy guarantees using fft,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.834184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1f72c801-0b2e-4f2b-a4f0-3a7cac9a4afd · outbound

This paper cites Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T15:54:59.548556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b90e5daf-6736-4a87-85a5-07527d86e442 · outbound

This paper cites Optimal accounting of differential privacy via characteristic function,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Optimal accounting of differential privacy via characteristic function,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.818925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 34512135-369c-4094-82c0-fea036540e68 · outbound

This paper cites Becker and R.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Becker and R

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T15:54:59.558406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56aa0ee0-f795-4bd1-ae57-ed5205a1cb1e · outbound

This paper cites Locally differentially pri- vate protocols for frequency estimation,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Locally differentially pri- vate protocols for frequency estimation,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T15:54:59.563257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24a7620f-f944-4663-a82a-4adf0e4539f3 · outbound

This paper cites Rappor: Randomized aggregatable privacy-preserving ordinal response,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Rappor: Randomized aggregatable privacy-preserving ordinal response,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T15:54:59.567721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37337d5e-af6d-43f8-87ab-c3689e76c131 · outbound

This paper cites Learning with privacy at scale,.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach Learning with privacy at scale,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.782439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e0f87f0c-739a-4f62-a011-d8e835edc231 · outbound

This paper cites The proposed approach provides a creative solution to this issue by adapting the noise distribution based on desired constraints on the query output utility.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach The proposed approach provides a creative solution to this issue by adapting the noise distribution based on desired constraints on the query output utility

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.766045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9b68c33e-7b30-45ad-979a-2bf28c750e12 · outbound

This paper cites The authors’ approach is tech- nically novel and provides increased output utility compared to SOTA without the need for relaxation of the DP guarantees.

Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach The authors’ approach is tech- nically novel and provides increased output utility compared to SOTA without the need for relaxation of the DP guarantees

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:54:59.749956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.