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Paper Citation Record · LEDGER

Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2404.03233.

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

pith.paper-citation-record.v1
2404.03233 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:01:31.956527Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T17:15:43.733930Z

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 3bcfab7a-9083-464f-9156-15ae977f4935 · inbound

AdaProb: Efficient Machine Unlearning via Adaptive Probability cites this paper.

AdaProb: Efficient Machine Unlearning via Adaptive Probability Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-23T17:15:43.737405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T17:13:32.169844Z digest=sha256:eb9d950c12005424d1cfed3822094ffac66ff2d444398625bd4e48f93a5cb4af

Observation 1d0cf9ed-84d0-4b38-b638-a1444eb2a3d1 · inbound

BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap cites this paper.

BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T13:01:31.956527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:01:31.956527Z digest=sha256:8c931d54669da0c03891b35836da0610cca8461c63ce332d99f056d1f826d1df

Observation c2cdce6c-588d-4f46-beff-65364f6438ff · inbound

Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses cites this paper.

Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-05T15:39:55.272015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:39:55.272015Z digest=sha256:4fc20bc796b9e750f15dd36c80b7effeae811ea842adb22326d8d98c7b69d97f

Observation 73906e04-d433-4e11-ba98-c0573d4ce187 · inbound

RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories cites this paper.

RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-03T18:41:35.335649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T18:41:35.335649Z digest=sha256:3c7384d2ba25a324c121cdc5d1b3ce6c4ea9a4945c95b89b84eb7cd17f4849e0

Observation 29c15701-07c7-47ab-b2c6-cf6e75154bf2 · inbound

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? cites this paper.

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-14T19:29:23.821078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-14T19:28:31.180563Z digest=sha256:0e33057ad3ac83c4dfb7f6d660d593d166f579e80f15ca9513748d9457496128