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

PrivateHub is a contrastive diffusion model designed to generate synthetic multi‑sensor data that protects private user activities while keeping non‑private applications detectable. It operates in two stages: App‑Conditioned Pre‑training, which conditions the model on application embeddings, and App‑Aware Fine‑tuning, which uses contrastive learning to separate private from non‑private data. Experiments on three real‑world datasets demonstrate that PrivateHub reduces private‑application inference accuracy by 40–50% without harming non‑private performance and remains robust even when attackers retrain on the synthetic data.

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• rag
• diffusion
• fine-tuning
• safety