用积分决策梯度提升雷达人体行为识别的隐私保护能力
Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity Recognition
- 基于积分决策梯度生成隐私敏感度，动态加噪实现差分隐私
- 在标签仅知和影子模型攻击下，隐私泄露风险降低60%以上
- 适合医疗健康领域需高精度又严控隐私的雷达监测系统
人体运动分析在健康监护和疾病早期发现中潜力巨大。雷达传感系统因其非接触式操作及可集成于现有Wi-Fi网络，且相比摄像头更少侵犯隐私而受到关注。然而，近期研究显示，可通过雷达步态模式高精度识别个体或性别，引发隐私担忧。本文分析雷达人体行为识别（HAR）系统的隐私漏洞，提出一种基于积分决策梯度（IDG）驱动的差分隐私（DP）方法——IDG-DP。通过设计基于CNN的HAR模型，评估其在多种攻击者信息可访问程度下的鲁棒性，尤其针对黑盒成员推断攻击（MIA）。实验表明，该方法在所有场景下均能有效缓解隐私攻击，同时保持良好性能，尤其在标签仅知和影子模型攻击下表现突出。本工作为医疗环境中雷达辅助人体行为识别的精准性与隐私保护之间的平衡提供了关键解决方案。
原文摘要 · Abstract (English)
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.
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