ABSTRACT
Compressive sensing (CS) is attractive in long-term electrocardiography (ECG) telemonitoring to extend life-time for resource-constrained wireless wearable sensors. However, the availability of transmitted personal information has posed great concerns for potential privacy leakage. Moreover, the traditional CS-based security frameworks focus on secured signal recovery instead of privacy-preserving data analytics; hence, they provide only computational secrecy and have impractically high complexities for decryption. In this paper, to protect privacy from an information-theoretic perspective while delivering the classification capability, we propose a low-complexity framework of Privacy-Preserving Compressive Analysis (PPCA) based on subspace-based representation. The subspace-based dictionary is used for both encrypting and decoding the CS measurements online, and it is built by dividing signal space into discriminative and complementary subspace offline. The encrypted signal is unreconstructable even if the eavesdropper cracks the measurement matrix and the dictionary. PPCA is implemented in ECG-based atrial fibrillation detection. It can reduce the mutual information by 1.98 bits via encrypting measurements with signal-dependent noise at 1 dB, while the classification accuracy remains 96.05% with the decoding matrix. Furthermore, by decoding via matrix-vector product, rather than sparse coding, this computational complexity of PPCA is 341 times fewer compared with the traditional CS-based security.
