联合感知与通信系统中窃听的操作性泄漏度量
An Operational Leakage Metric for Eavesdropping in Joint Sensing and Communications Systems
- Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
- Peng Cheng Laboratory（鹏城实验室）
- Dongguan University of Technology（东莞理工学院）
- National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院移动通信国家重点实验室）
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens（雅典国立卡波季斯特里安大学信息与电信系）
- Department of Electronic and Electrical Engineering, University College London（伦敦大学学院电子电气工程系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对ISAC系统中窃听的双重风险，提出操作性泄漏度量，用条件拉普拉斯近似高效评估Eve的最优重建或估计概率，数值验证其随SNR增大且优于传统度量。
AI中文摘要：
在即将到来的集成感知与通信（ISAC）系统中，安全性取决于窃听者（Eve）能否从与合法接收者共同的观测中恢复数据符号或推断感知信息。然而，现有的信息与估计度量无法在有限观测记录下量化这种双重风险。本文提出了一种新颖的操作性泄漏度量，该度量表示在贝叶斯意义上，Eve在规定的失真范围内重建通信符号或估计感知参数的最优概率。通过证明先验对抗成功概率将侧信息的影响与观测的影响分离，我们提出了一种条件拉普拉斯近似方法，以高效评估所提出的度量并联合量化对感知和通信功能的窃听风险。数值结果表明，对抗性观测所引发的风险随Eve的信噪比（SNR）增加而增大，传统度量无法唯一确定有限记录下的对抗成功概率，且所提出的条件高斯近似与数值贝叶斯评估结果紧密吻合。
英文摘要：
In upcoming integrated sensing and communications (ISAC) systems, security depends on whether an eavesdropper (Eve) can recover data symbols or infer sensing information from a common observation with a legitimate receiver. However, existing information and estimation measures do not quantify this dual risk under finite observation records. This letter introduces the novel metric of operational leakage, which signifies Eve's optimal probability, in the Bayesian sense, of reconstructing communication symbols or estimating a sensing parameter within prescribed distortions. By showing that prior adversarial success probability separates the effect of side information from that of the observation, we propose a conditional Laplace approximation for efficiently evaluating the proposed metric and jointly quantifying the eavesdropping risk to the sensing and communication functionalities. Numerical results showcase that the risk enabled by adversarial observation increases with Eve's signal-to-noise ratio (SNR), conventional metrics do not uniquely determine finite-record adversarial success probability, and that the proposed conditional Gaussian approximation closely follows numerical Bayes evaluation.
