Dr. Hafiz Imtiaz

In this work, we propose an end-to-end privacy-preserving CSI-based HAR framework that integrates a Convolutional Neural Network (CNN) with a temporal attention mechanism. We perform extensive evaluations on multiple benchmark datasets consisting of varying distance and height factors, as well as different environmental conditions. Our baseline non-privacy-preserving CNN–Temporal attention model achieves state-of-the-art performance. Additionally, we incorporate differential privacy (DP) into the training pipeline — enabling rigorous privacy guarantees through controlled noise injection and gradient clipping. We evaluate the proposed framework’s privacy–utility trade-off and demonstrate that even a strong privacy protection can maintain excellent recognition accuracy. Our framework can progressively approach the non-privacy-preserving performance for some parameter regime. This work led to this RSC Digital Discovery publication.