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Paper Citation Record · LEDGER

SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.09481.

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

pith.paper-citation-record.v1
2404.09481 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:56:58.005683Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T21:25:34.757873Z

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 be186439-759e-4c59-a02f-8ef41794377b · inbound

SpaLLM-Guard: Pairing SMS Spam Detection Using Open-source and Commercial LLMs cites this paper.

SpaLLM-Guard: Pairing SMS Spam Detection Using Open-source and Commercial LLMs SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection

Reference 20

Resolution
verified exact
local_arxiv, observed 2026-08-10T21:25:34.764508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T21:25:34.454094Z digest=sha256:672680f6b3e0eb8c859871afdedbe4a9deabba8bb5bbb0e75ca048ef02ccdcf0

Observation 64b8c5d4-7009-468b-bca6-25b8a70c85a5 · inbound

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models cites this paper.

Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-16T00:56:58.005683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:56:58.005683Z digest=sha256:4228f71dfb9538259f4602ace1bb9752a8f7ba8b1f483c1324ec74a3788cd5a2