ZK-AdapterMark: Privacy-Preserving Provenance Evidence for LoRA Adapters

FedEAVE: federated evidence alignment and causal diagnosis for privacy-preserving personalized learning
Yan Zeng, Xuanyun Wang, Lai-Kuan Kong
Scientific Reports30 Oct 2026Peer-reviewedCC BY 4.0Crossref
Privacy-preserving synthetic data from a national CIDP registry: validation against real-world evidence
Alessio Signori, Erika Schirinzi, Fiore Manganelli +53
Frontiers in Neurology13 Oct 2026Peer-reviewedCC BY 4.0Full textNeurologyCrossrefPMC
A regulation-aware architecture for governing AI-generated content: provenance, labeling, privacy, safety, and institutional compliance
Iván Miguel García López, José Martín Molina Espinosa, María Soledad Ramírez Montoya +1
Frontiers in Artificial Intelligence21 Oct 2026Peer-reviewedCC BY 4.0Full textPMC
Source Locked Architecture V1.0: Preserving Source Integrity, Provenance, and Stateful Continuity in Language-Model Systems
Tara Callegari
Zenodo25 Oct 2026PreprintCC BY 4.0Full textZenodo
Causal-enhanced federated transfer learning framework for privacy-preserving cross-organizational strategic collaboration
Gaoyuan Fu
Scientific Reports22 Oct 2026Peer-reviewedCC BY-NC-ND 4.0Full textCrossref

Cite

BibTeX

@article{yu2026zkadaptermark,
  title = {{ZK-AdapterMark: Privacy-Preserving Provenance Evidence for LoRA Adapters}},
  author = {Yicheng Yu and Wangyu Wu and Renwen Ma},
  journal = {Entropy (Basel, Switzerland)},
  year = {2026},
  month = oct,
  url = {https://europepmc.org/article/PMC/PMC13703714},
  note = {CC BY},
}

RIS

TY  - JOUR
TI  - ZK-AdapterMark: Privacy-Preserving Provenance Evidence for LoRA Adapters
AU  - Yicheng Yu
AU  - Wangyu Wu
AU  - Renwen Ma
PY  - 2026
DA  - 2026/10/01
JO  - Entropy (Basel, Switzerland)
UR  - https://europepmc.org/article/PMC/PMC13703714
SN  - 1099-4300
ER  -

Related

Cite