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PrivateHub: Contrastive Diffusion Model for Private Sensor-Intensive Environment Data Generation

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Sensor-intensive environments enable many intelligent services by inferring user applications from heterogeneous data streams. However, not all applications should be exposed: users want some activities to stay private. This creates a tension between inferring applications for useful services and preventing unwanted inference. Existing approaches such as differential privacy and rule-based filtering protect individual streams but cannot address the privacy risk from cross-sensor inference. We introduce Privatehub, which uses contrastive learning within a diffusion model to generate synthetic m

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