ISAC systems introduce new privacy risks because an unintended sensing node may exploit the shared radio waveform to infer transmitter-related information even when the communication payload remains secure. This paper investigates transmitter privacy, defined as limiting unauthorized inference of transmitter-related information through channel estimation, in a RIS-aided multi-antenna wireless system with a transmitter, a legitimate receiver, a malicious sensor, and a RIS. The malicious sensor is assumed to estimate the transmitter--sensor channel, and the resulting channel state information can then support unauthorized sensing, inference, or related signal processing. To mitigate this threat, we consider a privacy-oriented design in which the transmitter adopts superposition-based signaling with a message signal and transmit-side artificial noise, while the RIS shapes the propagation environment in a privacy-aware manner. The channel-estimation performance at the malicious sensor is first analyzed under imperfect prior knowledge, and both the true and predicted mean-square-error expressions are derived. Based on this analysis, we formulate a joint active--passive beamforming design problem that maximizes the malicious sensor's predicted channel-estimation error subject to a communication quality-of-service constraint, a transmit-power budget, and the unit-modulus constraints of the RIS. The resulting non-convex problem is handled through a numerically efficient alternating-optimization framework based on an augmented Lagrangian reformulation. Numerical results show that RIS-assisted propagation shaping can substantially degrade unauthorized channel estimation relative to the non-RIS case while preserving reliable communication, and further show that the privacy gains also improve a more direct sensing metric, namely the malicious sensor's angle-of-arrival estimation accuracy.

翻译：集成感知与通信（ISAC）系统引入了新的隐私风险：即使通信载荷保持安全，非预期感知节点也可能利用共享无线电波形推断发射机相关信息。本文研究发射机隐私问题，其定义为通过信道估计限制对发射机相关信息的非授权推断，背景为包含一个发射机、一个合法接收机、一个恶意传感器和一个可重构智能表面（RIS）的RIS辅助多天线无线系统。假设恶意传感器估计发射机-传感器信道，所得到的信道状态信息可用于非授权感知、推断或相关信号处理。为缓解这一威胁，我们考虑一种面向隐私的优化设计：发射机采用基于叠加的信号方案（包含消息信号和发射端人工噪声），同时RIS以隐私感知方式塑造传播环境。首先在非完美先验知识下分析恶意传感器的信道估计性能，推导出真实和预测均方误差表达式。基于此分析，我们构建了一个联合有源-无源波束赋形优化问题，在满足通信服务质量约束、发射功率预算和RIS单位模约束的条件下，最大化恶意传感器的预测信道估计误差。该非凸问题通过基于增广拉格朗日重构的数值高效交替优化框架求解。数值结果表明，相比无RIS情形，RIS辅助的传播塑造能在维持可靠通信的同时显著降低非授权信道估计性能，并进一步表明隐私增益还能改善更直接的感知指标——恶意传感器的到达角估计精度。