Millimeter-Wave (mmWave) radar enables camera-free gesture recognition for Internet of Things (IoT) interfaces, with robustness to lighting variations and partial occlusions. However, recent studies reveal that its data can inadvertently encode biometric signatures, raising critical privacy challenges for IoT applications. In particular, we demonstrate that mmWave radar point cloud data can leak identity-related information in the absence of explicit identity labels. To address this risk, we propose {ImmCOGNITO}, a graph-based autoencoder that transforms radar gesture point clouds to preserve gesture-relevant structure while suppressing identity cues. The encoder first constructs a directed graph for each sequence using Temporal Graph KNN. Edges are defined to capture inter-frame temporal dynamics. A message-passing neural network with multi-head self-attention then aggregates local and global spatio-temporal context, and the global max-pooled feature is concatenated with the original features. The decoder then reconstructs a minimally perturbed point cloud that retains gesture discriminative attributes while achieving de-identification. Training jointly optimizes reconstruction, gesture-preservation, and de-identification objectives. Evaluations on two public datasets, PantoRad and MHomeGes, show that ImmCOGNITO substantially reduces identification accuracy while maintaining high gesture recognition performance.

翻译：毫米波（mmWave）雷达为物联网（IoT）界面提供了无需摄像头的手势识别能力，其具备光照变化鲁棒性及对局部遮挡的适应性。然而，近期研究表明其数据可能无意中编码生物特征信息，这为物联网应用带来了严峻的隐私挑战。我们特别论证了在缺乏显式身份标签的情况下，毫米波雷达点云数据仍可能泄露与身份相关的信息。为应对此风险，本文提出{ImmCOGNITO}——一种基于图的自编码器，通过对雷达手势点云进行变换，在保留手势相关结构的同时抑制身份特征。编码器首先使用时序图KNN为每个序列构建有向图，其中定义的边用于捕捉帧间时序动态。随后，采用多头自注意力机制的消息传递神经网络聚合局部与全局时空上下文信息，并将全局最大池化特征与原始特征拼接。解码器进而重构出经最小扰动的点云，在实现去身份化的同时保持手势判别属性。训练过程联合优化重构损失、手势保持目标与去身份化目标。在PantoRad和MHomeGes两个公开数据集上的评估表明，ImmCOGNITO在显著降低身份识别准确率的同时，仍能保持优异的手势识别性能。