pith. sign in

Integrity report for Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2605.02372

0Critical
0Advisory
3Detectors run
2026-05-20Last checked

Paper page arXiv integrity.json

Detector runs

ai_meta_artifact completed v1.0.0 · findings 0 · 2026-05-20 15:41:40.449531+00:00
doi_title_agreement completed v1.0.0 · findings 0 · 2026-05-20 03:31:22.473934+00:00
doi_compliance completed v1.0.0 · findings 0 · 2026-05-19 16:27:24.632865+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/2605.02372/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.