AI RESEARCH
CFD-HAR: User-controllable Privacy through Conditional Feature Disentanglement
arXiv CS.LG
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ArXi:2603.11526v1 Announce Type: new Modern wearable and mobile devices are equipped with inertial measurement units (IMUs). Human Activity Recognition (HAR) applications running on such devices use machine-learning-based, data-driven techniques that leverage such sensor data. However, sensor-data-driven HAR deployments face two critical challenges: protecting sensitive user information embedded in sensor data in accordance with users' privacy preferences and maintaining high recognition performance with limited labeled samples.