发表机构
Aristotle University of Thessaloniki; University College London（色雷斯大学; 伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种鲁棒协方差设计框架，在ISAC网络中联合保护机密数据和方向信息，通过贝叶斯估计和欺骗响应，在窃听者角度不确定性下平衡保密性、感知精度、隐私与欺骗能力。
AI 中文摘要
本文研究了集成感知与通信（ISAC）网络中机密数据和合法用户方向信息的联合保护问题。我们考虑一个多用户下行链路场景，其中被动多天线窃听者（Eves）试图解码机密信号，同时利用合法用户的反射进行未经授权的角度感知。为应对这两种威胁，我们开发了一个鲁棒的协方差设计框架，该框架联合限制窃听者的解码能力，保持发射机在估计窃听者方向时的准确性，并将主要的被动感知响应转向指定的欺骗方向。贝叶斯角度先验和相应的贝叶斯克拉美-罗界（BCRB）表征了发射机的窃听者角度估计精度。角度不确定性通过几何一致的样本表示，使得每个候选窃听者方向共同确定相应的发射机-窃听者信道、用户-窃听者方位和欺骗方向。所得到的设计在最差用户保密性、感知精度、感知隐私和欺骗能力之间取得平衡。鲁棒的窃听者解码约束通过具有样本间间隔的有限充分条件来处理，而连续的幽灵主导则通过区间平方和（SOS）约束来强制执行。所得到的非凸问题通过逐次凸逼近（SCA）和半定松弛来解决。数值结果表明，所提出的设计在窃听者角度不确定性下有效保持保密性和感知隐私，在单窃听者和多窃听者场景中提供受控的角度欺骗，并且优于基准方案。
英文摘要
This paper investigates the joint protection of confidential data and legitimate-user directional information in integrated sensing and communication (ISAC) networks. We consider a multiuser downlink in which passive multi-antenna eavesdroppers (Eves) attempt to decode confidential signals while exploiting legitimate-user reflections for unauthorized angular sensing. To address both threats, we develop a robust covariance-design framework that jointly limits Eve decoding, maintains the transmitter's accuracy in estimating the Eves' directions and shifts the dominant passive-sensing response toward prescribed deceptive directions. A Bayesian angular prior and the corresponding Bayesian Cramer-Rao bound (BCRB) characterize the transmitter's eavesdropper-angle estimation accuracy. Angular uncertainty is represented through geometry-consistent samples, such that each candidate Eve direction jointly determines the corresponding transmitter-Eve channel, user-Eve bearing, and deceptive direction. The resulting design balances worst-user secrecy, sensing accuracy, sensing privacy, and deception power. Robust Eve-decoding constraints are handled through finite sufficient conditions with intersample margins, while continuous ghost dominance is enforced using interval sum-of-squares (SOS) constraints. The resulting nonconvex problem is addressed through successive convex approximation (SCA) and semidefinite relaxation. Numerical results show that the proposed design effectively preserves secrecy and sensing privacy under Eve-angle uncertainty, provides controlled angular deception in single- and multiple-Eve scenarios, and outperforms benchmarks.
Comments15 pages, 9 figures
