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

2026-09-02

AI Summary

为解决传感器密集环境中的隐私保护问题，提出PrivateHub方法，通过对比学习和扩散模型生成合成多传感器数据流，有效隐藏私密活动同时保持非私密应用的可检测性。

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 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. Privatehub has two stages: App-Conditioned Pre-training (ACP), which conditions the model on multi-sensor data with application embeddings, and App-Aware Fine-tuning (AAF), which separates private from non-private data via contrastive learning. We also define a threat model for the multi-sensor sharing setting. Experiments on three real-world multi-sensor datasets show Privatehub lowers private-application accuracy by 40 to 50\% without hurting non-private performance, and stays robust when the attacker retrains on the synthetic data.

Problem

• Private Sensor-Intensive Environment
• Cross-sensor Inference
• Privacy Risk

Innovation

• contrastive learning
• diffusion model
• multi-sensor data
• privacy preservation
• synthetic data generation