Secure Tracking of Patient's Vital Signs Using CSI-Based Homomorphic Encryption-Enabled Deep Learning Framework.

Abstract

Preserving patient privacy in digital healthcare systems is a critical challenge, particularly in non-intrusive monitoring applications. This paper introduces VitalCrypt, a novel framework for secure and real-time vital sign monitoring that combines Channel State Information (CSI) with homomorphic encryption and lightweight deep learning. Homomorphic encryption enables computations directly on encrypted data, ensuring data confidentiality throughout the processing pipeline. The framework incorporates well-established signal preprocessing techniques, such as Hampel, Savitzky-Golay, and elliptic filters for noise removal, with Principal Component Analysis (PCA) for dimensionality reduction. The Power Spectral Density (PSD) of these refined signals is used as features, which are then fed into a lightweight neural network optimized with encryption-compatible activation functions for classification. The system effectively classifies breathing and heart rates while maintaining compatibility with homomorphic encryption schemes. Experimental evaluations were conducted using a publicly available dataset. The results demonstrated exceptional accuracy, achieving 99.46% for breathing rate classification on plain data and 99.44% on encrypted data, with negligible performance degradation despite increased runtime due to encryption. The results of heart rate classification are also discussed. The framework processes encrypted data at approximately seven times the latency of plain data; however, this trade-off is justified by the substantial privacy benefits attained. VitalCrypt showcases the potential of secure, privacy-preserving deep learning applications in healthcare, addressing critical challenges in real-time, non-intrusive patient monitoring. By balancing high accuracy and data confidentiality, this framework provides a scalable solution for healthcare applications, including remote monitoring and clinical diagnostics.