Respiratory monitoring is an extremely important task in modern medical services. Due to its significant advantages, e.g., non-contact, radar-based respiratory monitoring has attracted widespread attention from both academia and industry. Unfortunately, though it can achieve high monitoring accuracy, consumer electronics-grade radar data inevitably contains User-sensitive Identity Information (USI), which may be maliciously used and further lead to privacy leakage. To track these challenges, by variational mode decomposition (VMD) and adversarial loss-based encryption, we propose a novel Trusted Respiratory Monitoring paradigm, Tru-RM, to perform automated respiratory monitoring through radio signals while effectively anonymizing USI. The key enablers of Tru-RM are Attribute Feature Decoupling (AFD), Flexible Perturbation Encryptor (FPE), and robust Perturbation Tolerable Network (PTN) used for attribute decomposition, identity encryption, and perturbed respiratory monitoring, respectively. Specifically, AFD is designed to decompose the raw radar signals into the universal respiratory component, the personal difference component, and other unrelated components. Then, by using large noise to drown out the other unrelated components, and the phase noise algorithm with a learning intensity parameter to eliminate USI in the personal difference component, FPE is designed to achieve complete user identity information encryption without affecting respiratory features. Finally, by designing the transferred generalized domain-independent network, PTN is employed to accurately detect respiration when waveforms change significantly. Extensive experiments based on various detection distances, respiratory patterns, and durations demonstrate the superior performance of Tru-RM on strong anonymity of USI, and high detection accuracy of perturbed respiratory waveforms.

翻译：呼吸监测是现代医疗服务中极为重要的任务。基于雷达的呼吸监测因其非接触等显著优势，引起了学术界和工业界的广泛关注。然而，尽管能够实现较高的监测精度，消费级雷达数据不可避免地包含用户敏感身份信息，这些信息可能被恶意利用并导致隐私泄露。为应对这些挑战，本研究通过变分模态分解和基于对抗损失的加密技术，提出了一种新颖的可信呼吸监测范式Tru-RM，该范式能够通过射频信号实现自动化呼吸监测，同时有效匿名化用户敏感身份信息。Tru-RM的核心组件包括分别用于属性解耦、身份加密和抗扰动呼吸监测的属性特征解耦模块、灵活扰动加密器和鲁棒扰动容忍网络。具体而言，属性特征解耦模块旨在将原始雷达信号分解为通用呼吸分量、个体差异分量及其他无关分量。随后，灵活扰动加密器通过大噪声淹没其他无关分量，并采用带学习强度参数的相位噪声算法消除个体差异分量中的用户敏感身份信息，从而在不影响呼吸特征的前提下实现完整的用户身份信息加密。最后，通过设计迁移的广义域无关网络，扰动容忍网络能够在波形显著变化时准确检测呼吸状态。基于不同检测距离、呼吸模式和持续时间的广泛实验表明，Tru-RM在实现用户敏感身份信息的强匿名性和对扰动呼吸波形的高检测精度方面均表现出优越性能。