Human motion analysis offers significant potential for healthcare monitoring and early detection of diseases. The advent of radar-based sensing systems has captured the spotlight for they are able to operate without physical contact and they can integrate with pre-existing Wi-Fi networks. They are also seen as less privacy-invasive compared to camera-based systems. However, recent research has shown high accuracy in recognizing subjects or gender from radar gait patterns, raising privacy concerns. This study addresses these issues by investigating privacy vulnerabilities in radar-based Human Activity Recognition (HAR) systems and proposing a novel method for privacy preservation using Differential Privacy (DP) driven by attributions derived with Integrated Decision Gradient (IDG) algorithm. We investigate Black-box Membership Inference Attack (MIA) Models in HAR settings across various levels of attacker-accessible information. We extensively evaluated the effectiveness of the proposed IDG-DP method by designing a CNN-based HAR model and rigorously assessing its resilience against MIAs. Experimental results demonstrate the potential of IDG-DP in mitigating privacy attacks while maintaining utility across all settings, particularly excelling against label-only and shadow model black-box MIA attacks. This work represents a crucial step towards balancing the need for effective radar-based HAR with robust privacy protection in healthcare environments.

翻译：人体运动分析在健康监测与疾病早期检测方面展现出巨大潜力。基于雷达的传感系统因其无需物理接触即可工作，并能与现有Wi-Fi网络集成而备受关注。与基于摄像头的系统相比，它们也被视为对隐私侵入性较低。然而，近期研究表明，通过雷达步态模式识别个体或性别的准确率很高，这引发了隐私担忧。本研究通过探究基于雷达的人体活动识别（HAR）系统中的隐私漏洞，并提出一种利用集成决策梯度（IDG）算法驱动的归因差分隐私（DP）进行隐私保护的新方法，以应对这些问题。我们在不同攻击者可获取信息层级下，研究了HAR场景中的黑盒成员推断攻击（MIA）模型。通过设计一个基于CNN的HAR模型，并严格评估其抵御MIA的能力，我们全面评估了所提出的IDG-DP方法的有效性。实验结果表明，IDG-DP在保持所有场景下实用性的同时，具有缓解隐私攻击的潜力，尤其在应对仅标签和影子模型黑盒MIA攻击方面表现优异。这项工作代表了在医疗环境中平衡有效雷达HAR需求与强健隐私保护的关键一步。