Low probability of detection (LPD) has recently emerged as a means to enhance the privacy and security of wireless networks. Unlike existing wireless security techniques, LPD measures aim to conceal the entire existence of wireless communication instead of safeguarding the information transmitted from users. Motivated by LPD communication, in this paper, we study a privacy-preserving and distributed framework based on graph neural networks to minimise the detectability of a wireless ad-hoc network as a whole and predict an optimal communication region for each node in the wireless network, allowing them to communicate while remaining undetected from external actors. We also demonstrate the effectiveness of the proposed method in terms of two performance measures, i.e., mean absolute error and median absolute error.

翻译：低检测概率（LPD）近来已成为增强无线网络隐私与安全性的一种手段。与现有无线安全技术不同，LPD措施旨在隐藏无线通信的整个存在性，而非保护用户传输的信息。受LPD通信启发，本文研究了一种基于图神经网络的隐私保护分布式框架，以最小化无线自组织网络整体的可检测性，并为无线网络中各节点预测最优通信区域，使其在与外部行为者保持不可检测状态的同时进行通信。我们还通过平均绝对误差和中位数绝对误差这两个性能指标，验证了所提方法的有效性。