Privacy-Protection of Reference Signals via Transparent Artificial Multipath for ISAC

Abstract

The integration of sensing capabilities into 6G wireless networks, known as integrated sensing and communication (ISAC), introduces severe privacy risks by enabling unauthorized user localization through the eavesdropping reference signals. Existing physical-layer defense strategies, such as artificial noise or conventional artificial multipath, suffer from critical limitations including significant communication performance degradation, reliance on unrealistic assumptions about adversarial channel state information (CSI), and increased system overhead. To address these challenges, this paper proposes a novel transparent artificial multipath (TAM) framework. It employs constant-envelope precoding and combining matrices to transparently encrypt reference signals in the frequency domain. This design ensures that legitimate base stations can perform channel estimation and localization without any performance loss, while adversaries receive a deliberately distorted channel impulse response, preventing accurate user localization and tracking. The proposed method is power-efficient, requires no prior knowledge of the adversarial CSI, and incurs negligible additional dynamic signaling overhead. Furthermore, we present a scalable multi-user extension that allows sharing the time-frequency resources without interference. Both theoretical analysis and extensive simulations demonstrate that TAM successfully misleads adversaries, inducing distance estimation errors of hundreds of meters, while fully maintaining the communication and localization performance for legitimate users. While physical-layer security solutions had a limited practical impact in various standards, the use of physical-layer techniques for privacy protection due to sensing is a necessity. We show that the proposed solution can be seamlessly integrated. into current 5G and future 6G standards.