Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2006.07134.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T15:54:59.548556Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T09:11:21.112481Z
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 1f72c801-0b2e-4f2b-a4f0-3a7cac9a4afd · inbound
Meeting Utility Constraints in Differential Privacy: A Privacy-Boosting Approach cites this paper.
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 2d50adeb-320e-48d8-b401-82530b1f77da · inbound
PrivacyGo: Privacy-Preserving Ad Measurement with Multidimensional Intersection cites this paper.
PrivacyGo: Privacy-Preserving Ad Measurement with Multidimensional Intersection Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:52:28.431733Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9a28b404-a34b-4734-bb05-84dd07d1dc9b · inbound
Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy cites this paper.
Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy Tight Differential Privacy for Discrete-Valued Mechanisms and for the Subsampled Gaussian Mechanism Using FFT
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T09:11:21.115058Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.