Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning
Geometric data perturbation enables one-shot representation sharing for privacy-preserving collaborative learning: each participant applies a secret distance-preserving transformation to its private data and uploads the resulting representation to a central analyst. We study analyst-participant collusion, in which a colluding participant discloses its data and transformation to help the analyst reconstruct another participant's data. Independent participant-specific transformations block direct inversion through a disclosed common transformation but leave uploads in incompatible coordinate systems, degrading pooled learning. Data Collaboration analysis restores compatibility by aligning t...
| Authors | Keiyu Nosaka, Yamato Suetake, Yuichi Takano et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 August 2026 |
| Last updated at source | 2 October 2026 |
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