Abstract
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.
Abstract (translated)
人体运动分析为健康监测和早期疾病检测提供了巨大的潜力。基于雷达的传感系统因其无需物理接触并能与现有的Wi-Fi网络集成而备受瞩目。它们还被认为比摄像机系统对隐私的侵犯较小。然而，最近的研究表明，从雷达步态模式中识别主体或性别的准确率很高，这引发了隐私问题的关注。本研究通过探讨基于雷达的人体活动识别（HAR）系统的隐私漏洞，并提出了一种新的使用差分隐私（DP）的方法来保护隐私，该方法由集成决策梯度（IDG）算法驱动的属性决定。我们调查了在不同级别攻击者可访问信息下的黑盒成员推断攻击（MIA）模型在HAR设置中的情况。通过设计基于CNN的HAR模型并严格评估其对MIAs的抵抗能力，我们广泛评估了所提出的IDG-DP方法的有效性。实验结果表明，IDG-DP在减轻隐私攻击的同时维持了所有设置下的效用，尤其在对抗标签仅有的和影子模型黑盒MIA攻击时表现出色。这项工作是平衡雷达HAR在医疗环境中的有效性和强健的隐私保护需求的关键一步。
URL
https://arxiv.org/abs/2411.02099
