Paper Citation Record · LEDGER

Auditing Approximate Machine Unlearning for Differentially Private Models

As of 7 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2508.18671.

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

pith.paper-citation-record.v1
2508.18671 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:22:55.084553Z

measured 33 of 33 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 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 fuzzy29
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9f663c6a-eaac-4a1c-8d29-36176958e021 · outbound

This paper cites B., MIRONOV, I., T ALWAR, K., AND ZHANG , L.

Auditing Approximate Machine Unlearning for Differentially Private Models B., MIRONOV, I., T ALWAR, K., AND ZHANG , L

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.447955Z

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.

Observation d85d257f-c034-4d36-8eec-acaef26bb55e · outbound

This paper cites Evaluations of machine learning privacy defenses are misleading.

Auditing Approximate Machine Unlearning for Differentially Private Models Evaluations of machine learning privacy defenses are misleading

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.437918Z

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.

Observation 0d217bcb-c3ba-4c3b-99dc-c4cb96aabc8b · outbound

This paper cites Evaluations of machine learning privacy defenses are misleading.

Auditing Approximate Machine Unlearning for Differentially Private Models Evaluations of machine learning privacy defenses are misleading

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.427423Z

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.

Observation c3a4f816-19d5-4bf5-8af0-28b4dcfd3d2c · outbound

This paper cites A., JIA, H., T RAVERS , A., Z HANG , B., L IE, D., AND PAPERNOT , N.

Auditing Approximate Machine Unlearning for Differentially Private Models A., JIA, H., T RAVERS , A., Z HANG , B., L IE, D., AND PAPERNOT , N

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.417182Z

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.

Observation d24e1ba0-89ad-4ee5-8b23-4294d1b8940c · outbound

This paper cites California consumer privacy act of 2018, 2018.

Auditing Approximate Machine Unlearning for Differentially Private Models California consumer privacy act of 2018, 2018

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.407021Z

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.

Observation 09de7c6a-cbf2-45ed-9825-46bb64044b34 · outbound

This paper cites Towards making systems forget with machine unlearning.

Auditing Approximate Machine Unlearning for Differentially Private Models Towards making systems forget with machine unlearning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.396622Z

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.

Observation b1ed5bc0-2441-4d6c-af48-4b961ae0383f · outbound

This paper cites Membership inference attacks from first principles.

Auditing Approximate Machine Unlearning for Differentially Private Models Membership inference attacks from first principles

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.386515Z

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.

Observation 491bc8cd-4c77-4d4b-8efb-7c9dea59077a · outbound

This paper cites The privacy onion effect: Memorization is relative.

Auditing Approximate Machine Unlearning for Differentially Private Models The privacy onion effect: Memorization is relative

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.376331Z

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.

Observation 4c923587-961c-4ff1-9594-53c0aada1352 · outbound

This paper cites When machine unlearning jeopardizes privacy.

Auditing Approximate Machine Unlearning for Differentially Private Models When machine unlearning jeopardizes privacy

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.366090Z

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.

Observation 06aa997a-34f8-4b90-a622-64eb6cf0f4b3 · outbound

This paper cites Differential privacy: A survey of results.

Auditing Approximate Machine Unlearning for Differentially Private Models Differential privacy: A survey of results

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.356498Z

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.

Observation d33f53b0-cff6-45fa-adff-e5586aa727e8 · outbound

This paper cites The algorithmic foundations of differential privacy.

Auditing Approximate Machine Unlearning for Differentially Private Models The algorithmic foundations of differential privacy

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.346637Z

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.

Observation d9e885e9-9310-46fe-a959-fc5ae7f09cbd · outbound

This paper cites Regulation (EU) 2016/679 of the European Parliament and of the Council, 2016.

Auditing Approximate Machine Unlearning for Differentially Private Models Regulation (EU) 2016/679 of the European Parliament and of the Council, 2016

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.337008Z

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.

Observation 3142c421-ccd3-4197-b01c-2cc680173c30 · outbound

This paper cites Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation.

Auditing Approximate Machine Unlearning for Differentially Private Models Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.327607Z

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.

Observation e5bc753e-e2bf-4f05-963f-ba92cc13a757 · outbound

This paper cites Fisher information as a measure of privacy: Preserving privacy of households with smart meters using batteries.

Auditing Approximate Machine Unlearning for Differentially Private Models Fisher information as a measure of privacy: Preserving privacy of households with smart meters using batteries

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.317824Z

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.

Observation 5a838197-f10d-4fad-bead-255c6bb33dfb · outbound

This paper cites Fast machine unlearning without retraining through selective synaptic dampening.

Auditing Approximate Machine Unlearning for Differentially Private Models Fast machine unlearning without retraining through selective synaptic dampening

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.308122Z

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.

Observation dbb6ce9f-1660-4fe5-8deb-8d61c7028f07 · outbound

This paper cites Eternal sunshine of the spotless net: Selective forgetting in deep networks.

Auditing Approximate Machine Unlearning for Differentially Private Models Eternal sunshine of the spotless net: Selective forgetting in deep networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.298495Z

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.

Observation cdbbb6d7-e18c-47f7-8123-c91ab4d4fc6d · outbound

This paper cites Demo: Ft-privacyscore: Personal- ized privacy scoring service for machine learning participation.

Auditing Approximate Machine Unlearning for Differentially Private Models Demo: Ft-privacyscore: Personal- ized privacy scoring service for machine learning participation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.288423Z

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.

Observation e0dd6449-7f81-4ff5-97c3-2ca36cb2e696 · outbound

This paper cites RecPS: Privacy Risk Scoring for Recommender Systems.

Auditing Approximate Machine Unlearning for Differentially Private Models RecPS: Privacy Risk Scoring for Recommender Systems

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-05T16:22:55.140664Z

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.

Observation fef43abc-0ddf-46ab-ba2c-73a3cd0f2c7c · outbound

This paper cites Auditing differentially private machine learning: How private is private sgd? Advances in Neural Information Processing Systems 33 (2020), 22205–22216.

Auditing Approximate Machine Unlearning for Differentially Private Models Auditing differentially private machine learning: How private is private sgd? Advances in Neural Information Processing Systems 33 (2020), 22205–22216

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.278410Z

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.

Observation f7e68696-5eeb-4a3c-9948-73685f6d11d3 · outbound

This paper cites The composition theorem for differential privacy.

Auditing Approximate Machine Unlearning for Differentially Private Models The composition theorem for differential privacy

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.268516Z

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.

Observation 8f262878-c02f-40bc-b05f-71ee373f1add · outbound

This paper cites C., A FROZ , S., M ILLER , B., SHANKAR , V., B ACHWANI , R., J OSEPH , A.

Auditing Approximate Machine Unlearning for Differentially Private Models C., A FROZ , S., M ILLER , B., SHANKAR , V., B ACHWANI , R., J OSEPH , A

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.259117Z

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.

Observation 5ebb6cd1-d519-42b7-aebd-df22470fb3dc · outbound

This paper cites Z., AND MALOOF , M.

Auditing Approximate Machine Unlearning for Differentially Private Models Z., AND MALOOF , M

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.249265Z

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.

Observation 3a3924cc-49f6-466a-ae4b-ae32cfd92da1 · outbound

This paper cites M., S ALMAN , H., AND M ˛ ADRY, A.

Auditing Approximate Machine Unlearning for Differentially Private Models M., S ALMAN , H., AND M ˛ ADRY, A

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.239183Z

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.

Observation d373d187-d33b-4c33-a395-c07c3bd05d7d · outbound

This paper cites Membership inference attacks against language models via neighbourhood comparison.

Auditing Approximate Machine Unlearning for Differentially Private Models Membership inference attacks against language models via neighbourhood comparison

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.228751Z

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.

Observation 8b073716-1e9b-4689-9397-727364fbaf60 · outbound

This paper cites Tight auditing of differen- tially private machine learning.

Auditing Approximate Machine Unlearning for Differentially Private Models Tight auditing of differen- tially private machine learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.217485Z

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.

Observation d655cced-50ce-4684-916d-9bd71fc31f0f · outbound

This paper cites A Survey of Machine Unlearning.

Auditing Approximate Machine Unlearning for Differentially Private Models A Survey of Machine Unlearning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:55.059845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49f78488-6821-4757-8013-2ac401056277 · outbound

This paper cites Personal Information Protection and Elec- tronic Documents Act, 2000.

Auditing Approximate Machine Unlearning for Differentially Private Models Personal Information Protection and Elec- tronic Documents Act, 2000

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.207143Z

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.

Observation a23d3d47-04cb-4b7a-a876-c25100585e1a · outbound

This paper cites Privacy auditing with one (1) training run.

Auditing Approximate Machine Unlearning for Differentially Private Models Privacy auditing with one (1) training run

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.196427Z

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.

Observation 7af0df3b-49c3-4254-9fed-89016fc945b0 · outbound

This paper cites Privacy auditing with one (1) training run.

Auditing Approximate Machine Unlearning for Differentially Private Models Privacy auditing with one (1) training run

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.185071Z

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.

Observation dca5b1f3-ef37-47be-b276-e7d16e92a553 · outbound

This paper cites Debugging Differential Privacy: A Case Study for Privacy Auditing.

Auditing Approximate Machine Unlearning for Differentially Private Models Debugging Differential Privacy: A Case Study for Privacy Auditing

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:55.073521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f3f64d1-a1f1-4d61-a7e3-cc6c00362d93 · outbound

This paper cites an unresolved cited work.

Auditing Approximate Machine Unlearning for Differentially Private Models Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:22:55.174140Z

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.

Observation 792d93bc-84ca-4558-90b3-5651399db3bd · outbound

This paper cites Machine unlearning: Solutions and challenges.

Auditing Approximate Machine Unlearning for Differentially Private Models Machine unlearning: Solutions and challenges

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.163422Z

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.

Observation c48833a5-adc5-402c-aaeb-151d2f22a822 · outbound

This paper cites privacy onion effect.

Auditing Approximate Machine Unlearning for Differentially Private Models privacy onion effect

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:22:55.152239Z

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.

Pith citing papers

No inbound Pith citation observations are available.