Class-Aware Adaptive Differential Privacy in Deep Learning for Sensor-Based Fall Detection
Fall detection is a critical task in healthcare, particularly for elderly people. Timely fall detection and treatment can prevent severe injuries. Sensor-based activity data can be used to detect fall. However, this data are highly sensitive and raises significant privacy concerns. Existing privacy approaches apply uniform noise across all training samples, which affects the prediction performance. To address this limitation, we propose a Class-Aware Adaptive Differential Privacy (CA-ADP) framework integrated with a hybrid 3D Convolutional Neural Network and Bidirectional Long Short-Term Memory (3D CNN-BiLSTM) architecture. The CA-ADP mechanism dynamically adjusts the magnitude of noise a...
| Authors | Joydeb Kumar Sana |
|---|---|
| Primary category | cs.CR |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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