基于加密的可穿戴手势识别的隐私保护隐秘通信
Privacy-Preserving Covert Communication Using Encrypted Wearable Gesture Recognition
AI总结：
本文提出了一种基于加密的隐私保护隐秘通信系统，利用加密的手势识别技术在可穿戴设备上实现安全、低努力的非语言交互，通过多方同态学习管道提高系统准确率并确保数据安全。
摘要：
在隐秘和安全关键的环境中，安全通信至关重要，因为口头交流可能会暴露用户的意图或操作背景。基于可穿戴设备的手势通信能够实现低努力、非语言交互，但现有系统会泄露运动数据、中间表示或推理输出到不可信的基础设施，从而允许意图推断、行为生物特征泄露和内部攻击。本文提出了一种隐私保护的手势隐秘通信系统，确保任何第三方都不会接触到原始传感器信号、学习特征或分类输出。该系统采用多方同态学习管道，直接在加密的运动数据上进行手势识别，防止对手推断手势语义、回放传感器轨迹或访问中间表示。据我们所知，这项工作是首次将加密的手势识别应用于基于可穿戴设备的隐秘通信环境。我们设计并评估了隐秘信号传递的触觉和视觉反馈机制，并使用600个来自商用智能手表的手势样本评估了该系统，实现了超过94.44%的分类准确率，证明了所提出系统的可行性，并展示了从高性能系统到资源受限边缘设备的实用部署可能性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
