Paper Citation Record · LEDGER

Privacy-Preserving Machine Learning: Methods, Challenges and Directions

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:2108.04417.

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

pith.paper-citation-record.v1
2108.04417 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:02:32.508565Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T07:37:45.320291Z

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 50cb02b7-ecbf-496a-b2d2-5a6dbdeb31c2 · inbound

Data Collaboration Analysis with Orthonormal Basis Selection and Alignment cites this paper.

Data Collaboration Analysis with Orthonormal Basis Selection and Alignment Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-24T03:38:50.175860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 275c42b7-3707-498b-8744-cd19dd410882 · inbound

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning cites this paper.

GraphTheft: Quantifying Privacy Risks in Graph Prompt Learning Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T15:02:32.508565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16ba7129-c6e0-4c24-8274-175f71b3c75d · inbound

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces cites this paper.

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T04:47:01.385847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a6cf8de-b0b3-43d1-ada3-7636eabba7f9 · inbound

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning cites this paper.

Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T04:21:45.667017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c37575f-91bc-4b4f-b946-5f6396bdfeb7 · inbound

Towards Data Governance of Frontier AI Models cites this paper.

Towards Data Governance of Frontier AI Models Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:54.271963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ca62495-e839-4a18-831d-c7645226a694 · inbound

Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions cites this paper.

Privacy-Preserving Large Language Models: Mechanisms, Applications, and Future Directions Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T20:02:34.449700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a38ef666-e0f2-4553-8452-2884abeea4c6 · inbound

Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment cites this paper.

Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T11:38:35.419901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9dffebe-583d-454f-b775-aa5d642a5268 · inbound

Privacy Preserving Properties of Vision Classifiers cites this paper.

Privacy Preserving Properties of Vision Classifiers Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T17:52:53.900981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acc363dd-ac97-4851-8e9e-aa1bf3e264e0 · inbound

RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility cites this paper.

RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:07:14.184539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c85340cf-acac-4f50-aead-880a0646d9b4 · inbound

A User-Centric, Privacy-Preserving, and Verifiable Ecosystem for Personal Data Management and Utilization cites this paper.

A User-Centric, Privacy-Preserving, and Verifiable Ecosystem for Personal Data Management and Utilization Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:52.961384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 277b96ed-ec0e-4b41-906d-2a270faba24e · inbound

FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility cites this paper.

FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T03:00:48.653006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 61eb1354-c5ce-4133-a0a0-1283f41d4443 · inbound

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI cites this paper.

Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T19:21:36.350529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3662ac51-a24d-4aa0-8e40-2fb2ee9b9664 · inbound

Understanding User Privacy Perceptions of GenAI Smartphones cites this paper.

Understanding User Privacy Perceptions of GenAI Smartphones Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:10:51.626769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3756e64a-3b9a-4294-ad72-43ce4a1e7f0c · inbound

All in One: A Unified Synthetic Data Pipeline for Multimodal Video Understanding cites this paper.

All in One: A Unified Synthetic Data Pipeline for Multimodal Video Understanding Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:31:03.883767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ae63e78-effa-4f65-9f1b-7ff38c48d552 · inbound

Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training cites this paper.

Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 191

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:46:03.873599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1e000401-e5e8-4931-882d-9cf30c99c065 · inbound

Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis cites this paper.

Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:33:50.308633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b4c27865-f635-4817-aaad-b91d270fef78 · inbound

Near-Exponential Convergence Rates for kNN Classification based on Boltzmann Margin cites this paper.

Near-Exponential Convergence Rates for kNN Classification based on Boltzmann Margin Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 111

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T07:37:45.321523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dbbe4e87-09d1-4c7c-8650-b7873168f6e3 · inbound

Combating Knowledge Corruption in Agent Systems: A Byzantine-Tolerant Secure Collaborative RAG Framework cites this paper.

Combating Knowledge Corruption in Agent Systems: A Byzantine-Tolerant Secure Collaborative RAG Framework Privacy-Preserving Machine Learning: Methods, Challenges and Directions

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T18:59:12.756789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.