Paper Citation Record · LEDGER

CrypTen: Secure Multi-Party Computation Meets Machine Learning

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2109.00984.

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

pith.paper-citation-record.v1
2109.00984 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:31:33.729910Z

measured 1 of 1 external citation measurements

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 b5eb256f-252c-4a77-8907-7e97d0ad4976 · inbound

Covert Attacks on Machine Learning Training in Passively Secure MPC cites this paper.

Covert Attacks on Machine Learning Training in Passively Secure MPC CrypTen: Secure Multi-Party Computation Meets Machine Learning

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T15:31:33.729910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+00:00.