PrivateHub: Contrastive Diffusion Model for Private Sensor-Intensive Environment Data Generation
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 multi-sensor streams that keep non-private applications detectable while concealing private ones. ...
| Authors | Jiechao Gao, Yuandong Pan, Jie Wang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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