Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2107.12342.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T20:28:52.401270Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-19T05:22:06.497335Z
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 ed9ef5bb-434f-47ea-917f-7f21551c175f · inbound
Efficient and High-Accuracy Private CNN Inference with Helper-Assisted Malicious Security cites this paper.
Efficient and High-Accuracy Private CNN Inference with Helper-Assisted Malicious Security Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T05:22:06.499932Z
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 c71d868b-9591-4d24-906a-fa6a155d438f · inbound
CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation cites this paper.
CrypTorch: PyTorch-based Auto-tuning Compiler for Machine Learning with Multi-party Computation Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T20:28:52.401270Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.