A Novel Framework for Transmitter Privacy in Integrated Sensing and Communication

2026-04-17

AI Summary

This work addresses transmitter privacy in reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication systems, where a malicious sensing node can exploit the shared waveform to perform channel estimation and thereby infer sensitive information. The paper introduces a novel definition of transmitter privacy as the limitation on unauthorized channel estimation and proposes a joint active-passive beamforming framework that superimposes artificial noise at the transmitter while cooperatively optimizing RIS phase shifts to actively disrupt the malicious node’s channel and angle-of-arrival estimation, without compromising legitimate communication quality. To tackle the resulting non-convex optimization problem, an alternating algorithm based on the augmented Lagrangian method is developed, along with a closed-form expression for the mean squared error of the malicious channel estimate under imperfect prior knowledge. Simulations demonstrate that the proposed scheme significantly degrades eavesdropping estimation accuracy while maintaining reliable communication, outperforming RIS-free baselines in privacy preservation.

Technology Category

• Machine Learning: Privacy
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• transmitter privacy
• integrated sensing and communication
• channel estimation
• RIS-aided wireless system
• unauthorized inference

Innovation

• transmitter privacy
• RIS-aided ISAC
• artificial noise
• channel estimation error
• joint active-passive beamforming