Paper Citation Record · LEDGER

Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2304.06929.

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

pith.paper-citation-record.v1
2304.06929 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:07:00.375041Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:57:23.359024Z

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 210ee578-1661-434f-a5ee-63cbb9cdf348 · inbound

Detecting Pretraining Data from Large Language Models cites this paper.

Detecting Pretraining Data from Large Language Models Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

Reference 111

Resolution: verified exact; arxiv_id, observed 2026-05-17T18:07:24.663589Z

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 a4319b43-54c9-4fb8-a75e-3b3961a76060 · inbound

Access Denied: Meaningful Data Access for Quantitative Algorithm Audits cites this paper.

Access Denied: Meaningful Data Access for Quantitative Algorithm Audits Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-09T19:07:00.375041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b9c37f5-b2e7-4f2f-b69f-f4ff68b13954 · inbound

Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI cites this paper.

Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:22:07.178471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9859ef92-b049-48ce-a8dd-e8e4c1151084 · inbound

Gaze3P: Gaze-Based Prediction of User-Perceived Privacy cites this paper.

Gaze3P: Gaze-Based Prediction of User-Perceived Privacy Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T21:18:39.909331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1f5bdd3-de1e-4d52-bc3a-683ceabc4ceb · inbound

TADP-RME: A Trust-Adaptive Differential Privacy Framework for Enhancing Reliability of Data-Driven Systems cites this paper.

TADP-RME: A Trust-Adaptive Differential Privacy Framework for Enhancing Reliability of Data-Driven Systems Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:30:56.990782Z

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 e6d08764-8e07-4bd0-8061-bc194b00160d · inbound

MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models cites this paper.

MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T20:57:23.360537Z

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.