标题： 变分秘密公共随机性提取 显示英文标题

标题： Variational Secret Common Randomness Extraction

摘要： 本文研究了在存在窃听者Eve的情况下，通过公开讨论从两个合法方Alice和Bob观察到的相关随机源中提取公共随机性（CR）或秘密密钥的问题。我们提出了一种实用的两阶段CR提取框架。在第一阶段，引入了变分概率量化（VPQ）步骤，其中Alice和Bob使用概率神经网络（NN）编码器将他们的观测结果映射到离散的、几乎均匀的随机变量（RVs），同时最大限度地减少信息泄露给Eve。这是通过结合变分学习目标和对抗训练来实现的。在第二阶段，使用码偏移构造的安全草图将编码器输出校正为相同的秘密密钥，其保密性由VPQ目标保证。作为代表性应用，我们研究了物理层密钥（PLK）生成。除了传统的依赖于信道互易性原理并需要双向信道探测的方法，从而导致较大的协议开销且在高移动性场景中不适用之外，我们提出了适用于集成感知与通信（ISAC）系统的基于感知的PLK生成方法，其中Alice和Bob测量的配对距离-角度（RA）图作为相关源。该想法通过端到端仿真和现实世界的软件定义无线电（SDR）测量得到了验证，包括Eve部分了解Bob位置的场景。结果证明了所提出的CR提取框架和基于感知的PLK生成方法的可行性和令人信服的性能。 显示英文摘要

摘要： This paper studies the problem of extracting common randomness (CR) or secret keys from correlated random sources observed by two legitimate parties, Alice and Bob, through public discussion in the presence of an eavesdropper, Eve. We propose a practical two-stage CR extraction framework. In the first stage, the variational probabilistic quantization (VPQ) step is introduced, where Alice and Bob employ probabilistic neural network (NN) encoders to map their observations into discrete, nearly uniform random variables (RVs) with high agreement probability while minimizing information leakage to Eve. This is realized through a variational learning objective combined with adversarial training. In the second stage, a secure sketch using code-offset construction reconciles the encoder outputs into identical secret keys, whose secrecy is guaranteed by the VPQ objective. As a representative application, we study physical layer key (PLK) generation. Beyond the traditional methods, which rely on the channel reciprocity principle and require two-way channel probing, thus suffering from large protocol overhead and being unsuitable in high mobility scenarios, we propose a sensing-based PLK generation method for integrated sensing and communications (ISAC) systems, where paired range-angle (RA) maps measured at Alice and Bob serve as correlated sources. The idea is verified through both end-to-end simulations and real-world software-defined radio (SDR) measurements, including scenarios where Eve has partial knowledge about Bob's position. The results demonstrate the feasibility and convincing performance of both the proposed CR extraction framework and sensing-based PLK generation method.