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Paper Citation Record · LEDGER

Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2301.09254.

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

pith.paper-citation-record.v1
2301.09254 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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:50:27.720334Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:58:11.292472Z

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 ae916d68-52f3-4fef-be72-5d98618d4ebd · inbound

TruncFormer: Private LLM Inference Using Only Truncations cites this paper.

TruncFormer: Private LLM Inference Using Only Truncations Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-12T04:50:27.720334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:50:27.720334Z digest=sha256:d79300b33fbf46b4dd2a73dbe9266410f7c6f012330310ba10a654b812adc58c

Observation 40b8672d-62fe-4da6-9f14-96d27ba4a705 · inbound

Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives cites this paper.

Towards Efficient Privacy-Preserving Machine Learning: A Systematic Review from Protocol, Model, and System Perspectives Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference

Reference 111

Resolution
verified exact
local_arxiv, observed 2026-08-06T15:58:11.295561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-06T15:58:10.612817Z digest=sha256:247ea7d03299a1861a63ab14ea531a6c293d1ffb72ec82f5ef3273e4b9ebded8