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

arXiv:2609.02958 · cs.CR, cs.AI · Submitted 2026-09-02 · Read on arXiv

cs.CR, cs.AI

Submitted: 2026-09-02

Updated: 2026-09-02

License: http://creativecommons.org/licenses/by/4.0/

The gist: Sensor-intensive environments enable many intelligent services by inferring user applications from heterogeneous data streams.

Terminology

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.

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