Integrated Identity Protection for Semantic Communications in Embodied IoT Systems
Yu Zhang, JingQi Liu, Shuangrui Zhao, Yuanyu Zhang, Yulong Shen, Xiaohong Jiang
Embodied Internet of Things (IoT) systems are promoting the adoption of task-oriented semantic communication (SemCom), in which the trustworthy task decisions require both semantic information reliability and source legitimacy. However, existing trustworthy designs in SemCom mainly focus on semantic content recovery and protection, with limited attention to source legitimacy. Motivated by this limitation, this paper establishes a new information exploitation paradigm for integrated SemCom-identity protection to address active source impersonation and passive identity inference by treating each SemCom signal as an inherent carrier of both task-relevant semantic information and transmitter physical identity information. We first examine these two information resources and then analyze their integration feasibility at the observation, processing, and decision levels. To exploit these resources for trustworthy decision making, we further develop a dual-branch identity protection mechanism under different operating modes. In the authentication mode, semantic recovery and physical-layer source verification are performed in parallel using the same received observation. In the concealment mode, identity exposure is suppressed while semantic recovery quality is maintained to support anonymous sensing reports. The resulting outputs are jointly interpreted to support task decisions under mode-specific identity protection requirements. Finally, two representative case studies on active source impersonation and passive identity inference are presented to evaluate the proposed framework.
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