Privacy-preserving data collection and recovery of compressive sensing

Hung, Tsung-Hsuan; Hsieh, Sung-Hsien; Lu, Chun-Shien

doi:10.1109/chinasip.2015.7230447

Cited by 9 publications

(1 citation statement)

References 12 publications

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“…In [47], a lightweight privacy-preserving system has been introduced. However, to assure privacy, the sensors after each acquisition will need to send the sensing matrix along with the measurement vector (compressed) to the cloud.…”

Section: Privacy-preserving Cs-recovery Outsourcingmentioning

confidence: 99%

Privacy Assured Recovery of Compressively Sensed ECG Signals

et al. 2022

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In the areas of communications engineering and biomedical engineering, cloud computing for storing data and running complex algorithms have been steadily increasing due to the increase in internet of things and connected health. As connected IoT devices such as wearable ECG recorders generally have less storage and computational capacity, acquired signals get sent to a remote center for storage and possible analysis on demand. Recently, compressive sensing has been used as a secure, energy-efficient and fast method of signal sampling in such recorders. In this paper, we propose a secure procedure to shift away the total recovery of compressively sensed measurement to cloud and introduce a privacy-assured signal recovery technique in the cloud. We present a fast, and lightweight encryption for secure CS recovery outsourcing that can be used in wearable devices, such as ECG Holter monitors. In the proposed technique, instead of full recovery of CS-compressed ECG signal in the cloud, to preserve privacy, an encrypted version of ECG signal is recovered by using a randomly bipolar permuted measurement matrix. The user with a key, decrypts the encrypted ECG from the cloud to obtain the original ECG signal at their end. We demonstrate our proposed method using the ECG signals available in the MIT-BIH Arrhythmia Database. We also demonstrate the strength of the proposed method against partial exposure of the key. Experimental results on client and cloud sides show our proposed method has lower complexity and consuming time compared to the recent related works, while maintaining the quality of outsourcing task in cloud.

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Section: Privacy-preserving Cs-recovery Outsourcingmentioning

confidence: 99%