Due to the inherent openness of wireless channels and the restriction of communication resources and energy supply, the privacy protection of the sensing data transmission in the security-critical Internet of Medical Things (IoMT) has become a great challenge. In order to guarantee the privacy of IoMT sensing and transmission in a wireless wiretap channel and reduce the power consumption, a privacy-aware sensing and transmission scheme with the name of sparse-learning-based encryption and recovery (SLER) is proposed. The sparse sensing signal is compressed and encrypted at the IoMT devices in the encryption stage and transmitted to the network coordinator or edge devices, where the sparse signal is accurately recovered via sparse learning in the decryption stage. The encryption stage is conducted based on compressed sensing. The decryption stage utilizes a model-based sparsity-aware deep neural network to accurately recover the sensing signal, whose sparse features are extracted to decrease the required size of measurement signals and increase the spectrum efficiency. The secrecy performance of the proposed SLER algorithm is theoretically analyzed. Experiments of electrocardiogram (ECG) signal transmission are performed as a typical IoMT application. The experimental results show that the proposed scheme can effectively guarantee the transmission secrecy against eavesdropping, while improving the spectrum efficiency and energy efficiency compared to other existing methods.

中文翻译：

---

基于学习的高效稀疏感知和隐私感知 IoMT 恢复


由于无线信道固有的开放性以及通信资源和能源供应的限制，安全关键的医疗物联网（IoMT）中传感数据传输的隐私保护成为了巨大的挑战。为了保证无线窃听通道中物联网传感和传输的隐私性并降低功耗，提出了一种基于稀疏学习的加密和恢复（SLER）的隐私感知传感和传输方案。稀疏感知信号在加密阶段在物联网设备处被压缩和加密，并传输到网络协调器或边缘设备，在解密阶段通过稀疏学习准确地恢复稀疏信号。加密阶段是基于压缩感知进行的。解密阶段利用基于模型的稀疏感知深度神经网络来准确恢复传感信号，提取其稀疏特征以减少所需的测量信号大小并提高频谱效率。对所提出的SLER算法的保密性能进行了理论分析。心电图 (ECG) 信号传输实验作为典型的 IoMT 应用进行。实验结果表明，该方案能够有效保证传输保密性、防止窃听，同时相比其他现有方法提高了频谱效率和能量效率。