Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
CrypTen: Secure Multi-Party Computation Meets Machine Learning
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2109.00984.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T13:54:38.194312Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
62
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation b34cb67e-e6b4-4d85-bf81-34c275d5ea45 · inbound
Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation cites this paper.
Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation CrypTen: Secure Multi-Party Computation Meets Machine Learning
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T13:54:38.194312Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 00374dda-7683-4c38-8bc4-d3b86315a5aa · inbound
SecureV2X: An Efficient and Privacy-Preserving System for Vehicle-to-Everything (V2X) Applications cites this paper.
SecureV2X: An Efficient and Privacy-Preserving System for Vehicle-to-Everything (V2X) Applications CrypTen: Secure Multi-Party Computation Meets Machine Learning
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:01:09.828458Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb30c07a-724f-4fc5-8b14-4fe2529523f0 · inbound
Private, Verifiable, and Auditable AI Systems cites this paper.
Private, Verifiable, and Auditable AI Systems CrypTen: Secure Multi-Party Computation Meets Machine Learning
Reference 160
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T15:43:59.109829Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d4c15117-a51a-4a87-b5d0-9da349a6e43f · inbound
Privacy-Preserving Product-Quantized Approximate Nearest Neighbor Search Framework for Large-scale Datasets via A Hybrid of Fully Homomorphic Encryption and Trusted Execution Environment cites this paper.
Privacy-Preserving Product-Quantized Approximate Nearest Neighbor Search Framework for Large-scale Datasets via A Hybrid of Fully Homomorphic Encryption and Trusted Execution Environment CrypTen: Secure Multi-Party Computation Meets Machine Learning
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T11:05:09.205033Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation ded775c3-b20e-4b11-8725-d239acd6d5a8 · inbound
A Pragmatic Comparison of Cryptographic Computation Technologies for Machine Learning cites this paper.
A Pragmatic Comparison of Cryptographic Computation Technologies for Machine Learning CrypTen: Secure Multi-Party Computation Meets Machine Learning
Reference 50
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-08T17:33:46.775288Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.