Privacy-preserving human activity recognition using principal component-based wavelet CNN

Human activity recognition (HAR) is crucial in applications such as smart homes, interactive games, surveillance, security, and healthcare. In recent years, Channel State Information (CSI) data extracted from Wi-Fi signals has garnered significant interest for applications in HAR. This interest stems from CSI’s several advantages, including its immunity to illumination variations and environmental disturbances, and the elimination of the need for wearable devices. Despite being widely used, existing HAR system’s performance suffers when used in new environments without system improvement or retraining. This constraint can be overcome by gathering and annotating data from various locations, and then re-training the system. However, it is far from ideal from the privacy perspective, as the training algorithms access the data from different privacy-sensitive environments. This motivates us to design a reliable and robust privacy-preserving HAR system. In this work, we introduce a Differentially Private Principal Component-based Wavelet Convolutional Neural Network (DP-PCWCNN) that offers accurate and robust HAR performance across different environments, while preserving strict privacy constraints. We evaluate the performance of our proposed algorithm on two publicly available real datasets and demonstrate that our proposed system closely approximates the non-private system’s performance for some parameter choices.