Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2102.05975.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:00:08.812317Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-30T13:54:43.792518Z
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 281c7318-83d0-4674-844a-b61abd8eb707 · inbound
Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models cites this paper.
Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks
Reference 70
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:00:08.812317Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation da1a1a90-e613-48b0-a7b1-7420a5d24310 · inbound
Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling cites this paper.
Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T10:06:30.257950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation fb03c13b-4210-4347-8081-ec6a469797fc · inbound
Balancing Fairness, Privacy, and Accuracy: A Multitask Adversarial Framework for Centralized Data-Driven Systems cites this paper.
Balancing Fairness, Privacy, and Accuracy: A Multitask Adversarial Framework for Centralized Data-Driven Systems Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks
Reference 38
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T13:54:43.793990Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.