MCEN: A Multi-modal Compression and Encryption Network for Medical IoT

As the Medical Internet of Things (MIoT) is rapidly evolving, increasing wearable devices are applied to collect various physiological signals for the purpose of medical applications. Currently, it's challenging to process multi-modal physiological signals on wearable devices because of their limitations of energy and computing resources, as well as the threat of data privacy leakage. To address aheadmentioned problems, this paper designs a novel scheme to process multi-modal physiological signals, namely Multi-modal Compression and Encryption Network (M-CEN) with GAN and AE. The proposed MCEN accomplishes the encryption and compression of data by the adversarial property of GAN and the feature extraction property of AE. The proposed scheme aims to protect user privacy and reduce the consumption of energy and computing resources. In addition, the proposed MCEN is simulated using the Pulse Transit Time PPG dataset. The results show that the proposed scheme can well encrypt the data, and the compression performance is improved by 36.3% and 61.3% compared to the CAE and WT Algorithm respectively.