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

Abstract

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 in

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@misc{gao2026privatehub,
  title = {{PrivateHub: Contrastive Diffusion Model for Private Sensor-Intensive Environment Data Generation}},
  author = {Jiechao Gao and Yuandong Pan and Jie Wang and Michael Lepech and Bradford Campbell},
  year = {2026},
  month = sep,
  eprint = {2609.02958},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.02958}
}