AI 中文总结
该研究针对ISAC系统的感知窃听风险，提出利用环境物联网设备的AmbSentry系统，通过优化基站波束成形与AIoT反射调制，提升感知安全性并保障合法用户性能。
AI 中文摘要
集成感知与通信（ISAC）已成为6G网络的关键范式，实现频谱与硬件资源的协同融合，以最大化系统效率。然而，无线传输的固有开放性使ISAC系统面临严重安全风险，尤其是感知信息的隐私问题：未授权的感知窃听者可通过直接估计开放的感知回波信道提取敏感目标参数（如距离和速度），导致传统基于数据的保护技术失效。为缓解该威胁，本文提出AmbSentry——一种ISAC系统，通过利用自然分布的无源环境物联网（AIoT）设备防止感知信息泄露给窃听者。具体而言，这些AIoT设备被策略性配置为协作干扰器和虚假目标，向感知环境引入可控干扰。基于该系统，我们构建联合优化问题，在服务质量（QoS）约束下最大化窃听者处的集成旁瓣电平，从而降低感知窃听性能，同时维持合法接收方的感知与通信性能。由于该问题非凸，我们进一步开发高效迭代算法，基于Dinkelbach变换和块坐标下降法协同设计基站的发射波束成形与AIoT设备的反射调制。详细结果表明，AmbSentry显著提升感知安全性：与窃听者相比，合法感知接收方在检测概率上实现14-dB信噪比优势，估计误差降低百倍。
英文摘要
Integrated sensing and communication (ISAC) has emerged as a pivotal paradigm for 6G networks, enabling the synergistic convergence of spectral and hardware resources to maximize system efficiency. However, the inherent openness of wireless transmission exposes ISAC systems to critical security risks, particularly regarding the privacy of the sensing information. Unauthorized sensing eavesdroppers can extract sensitive target parameters (e.g., range and velocity) by directly estimating open sensing echo channels, rendering traditional data-based protection techniques ineffective. To mitigate this threat, this paper proposes AmbSentry, an ISAC system that prevents the leakage of sensing information to sensing eavesdroppers by harnessing naturally distributed passive ambient IoT (AIoT) devices. Specifically, these AIoT devices are strategically configured to act as cooperative jammers and ghost targets, introducing controllable interference into the sensing environment. Based on the proposed system, we formulate a joint optimization problem to maximize the integrated sidelobe level at the eavesdropper under quality-of-service (QoS) constraints, thereby degrading sensing eavesdropping performance while maintaining sensing and communication performance for legitimate receivers. Since the problem is non-convex, we further develop an efficient iterative algorithm to cooperatively design the transmit beamforming at the base station and the reflection modulations of the AIoT devices based on Dinkelbach transformation and block coordinate descent methods. The detailed results also demonstrate that AmbSentry significantly enhances sensing security, allowing the legitimate sensing receiver to achieve a 14-dB SNR advantage in detection probability and a hundred times lower estimation error compared to the eavesdropper.
