Secure communication is essential in covert and safety-critical settings where verbal interactions may expose user intent or operational context. Wearable gesture-based communication enables low-effort, nonverbal interaction, but existing systems leak motion data, intermediate representations, or inference outputs to untrusted infrastructure, enabling intent inference, behavioral biometric leakage, and insider attacks. This work proposes a privacy-preserving gesture-based covert communication system that ensures, no raw sensor signals, learned features, or classification outputs are exposed to any third-party. The system employs a multi-party homomorphic learning pipeline for gesture recognition directly over encrypted motion data, preventing adversaries from inferring gesture semantics, replaying sensor traces, or accessing intermediate representations. To our knowledge, this work is the first to apply encrypted gesture recognition in a wearable-based covert communication setting. We design and evaluate haptic and visual feedback mechanisms for covert signal delivery and evaluate the system using 600 gesture samples from a commodity smartwatch, achieving over 94.44% classification accuracy and demonstrating the feasibility of the proposed system with practical deployability from high-performance systems to resource-constrained edge devices.

翻译：在隐蔽及安全关键场景中，安全通信至关重要，因为言语交互可能暴露用户意图或操作上下文。基于可穿戴手势的通信能够实现低负荷的非语言交互，但现有系统会将运动数据、中间表示或推理输出泄露给不可信基础设施，从而导致意图推断、行为生物特征泄露和内部攻击。本文提出一种隐私保护的手势隐蔽通信系统，确保原始传感器信号、学习到的特征或分类输出均不会暴露给任何第三方。该系统采用多方同态学习流程，直接在加密运动数据上进行手势识别，防止对手推断手势语义、重放传感器轨迹或访问中间表示。据我们所知，本研究首次将加密手势识别应用于基于可穿戴设备的隐蔽通信场景。我们设计并评估了用于隐蔽信号传递的触觉与视觉反馈机制，并利用商用智能手表采集的600个手势样本对系统进行评估，实现了超过94.44%的分类准确率，证明了所提系统从高性能设备到资源受限边缘设备的实际部署可行性。