Paper Citation Record · LEDGER

ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2007.09339.

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

pith.paper-citation-record.v1
2007.09339 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:08:46.204987Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T19:05:00.908373Z

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 a03c6e73-a3b0-42d7-bd2c-cfeb89818ead · inbound

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study cites this paper.

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-08T15:08:46.204987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53edf611-dcc9-4e5e-b107-76474e68f6c6 · inbound

Maturity Framework for Enhancing Machine Learning Quality cites this paper.

Maturity Framework for Enhancing Machine Learning Quality ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T05:04:24.393139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44722f7a-4e19-4a2a-83f5-9ccb9c440d8e · inbound

Securing AI Systems: A Guide to Known Attacks and Impacts cites this paper.

Securing AI Systems: A Guide to Known Attacks and Impacts ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T21:50:26.839728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f3ab1f9-d208-4338-a9f1-0c052db54381 · inbound

Cascading and Proxy Membership Inference Attacks cites this paper.

Cascading and Proxy Membership Inference Attacks ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T12:53:53.182997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50115edc-a5ca-4ef1-87d8-ce5196ee949a · inbound

A Unified Perspective on Adversarial Membership Manipulation in Vision Models cites this paper.

A Unified Perspective on Adversarial Membership Manipulation in Vision Models ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:08:12.957744Z

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 28bc5231-c77e-4747-b88d-05e622304adc · inbound

FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics cites this paper.

FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:28:21.487651Z

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 e5075763-df69-494b-8f3d-2506eb863653 · inbound

FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics cites this paper.

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-06-30T19:05:00.910001Z

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 f9b2af2f-bf63-40e9-b59a-c26639ccdd4e · inbound

On Reliability of Efficient Membership Inference Vulnerability Evaluation cites this paper.

On Reliability of Efficient Membership Inference Vulnerability Evaluation ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:14:01.239455Z

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 ecd1ff98-2e9b-41f3-9730-1ff843bf2942 · inbound

Auditing Fairness-Privacy Trade-offs: Subpopulation-Level Effects of Fairness-Enhancing Algorithms cites this paper.

Auditing Fairness-Privacy Trade-offs: Subpopulation-Level Effects of Fairness-Enhancing Algorithms ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T01:41:45.999306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.