SOURCE-LINKED INTELLIGENCE
Semantic Privacy Protection with Utility Preservation for 3D Point Clouds
Point cloud data face serious semantic privacy risks during acquisition, transmission, and cross-institutional sharing. Existing methods mostly rely on geometric perturbation or destructive encryption, which can reduce the recognizability of the original class but often impair downstream usability. This paper proposes a class-transfer-based semantic encryption framework for point clouds, aiming to conceal original class information while preserving task utility and supporting authorized recovery. Specifically, we construct a unified latent space with a shared-backbone Normalizing Flow, and com
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T09:15:35.000Z
First collected: 2026-09-20T16:41:15.630Z. This is not the publication date.