Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications

2026-06-30

AI Summary

This work addresses a critical privacy vulnerability in relay-assisted semantic communication systems, where relay nodes—despite lacking access to the original data—can infer sensitive information from semantic representations, leading to severe privacy leakage. The study is the first to expose this semantic privacy risk and introduces an iterative adversarial training framework that leverages a deep learning–based semantic communication architecture. This framework dynamically optimizes the strategic interaction between the legitimate receiver and a relay-side eavesdropper, simultaneously preserving high semantic reconstruction fidelity at the intended destination while actively suppressing the relay’s capability to infer semantic content. Experimental results demonstrate that the proposed method consistently and significantly widens the gap in semantic accuracy between the legitimate receiver and the relay across diverse channel conditions, thereby achieving effective and covert privacy protection.

Abstract

Semantic communication (SemCom) has emerged as a promising paradigm in which the transmission of task-relevant information is prioritized over raw data, enabling efficient and robust communication under resource and channel constraints. In this paper, the privacy implications of relay-assisted SemCom systems are studied, where the intermediate relay node operates directly on learned latent representations. It is shown that the relay, even without access to source data, can reliably infer semantic meaning and reconstruct signals with performance comparable to that of the legitimate receiver, revealing a fundamental privacy vulnerability of semantic representations. To address this issue, an iterative adversarial training framework is proposed in which a strong, adaptively trained eavesdropper at the relay is explicitly accounted for. The proposed approach alternates between optimizing the relay's eavesdropping function and the legitimate system, resulting in representations that preserve semantic decoding performance at the intended receiver while degrading semantic inference at the relay. The semantic accuracy gap between the legitimate receiver and the eavesdropper is significantly enlarged across channel conditions. Importantly, this protection is achieved in a stealthy manner, with high reconstruction fidelity maintained while semantic leakage is selectively suppressed.

Problem

• Semantic Leakage
• Privacy Preservation
• Relay-Assisted Semantic Communications
• Semantic Communication
• Latent Representations

Innovation

• Semantic Communication
• Privacy Preservation
• Relay-Assisted System
• Adversarial Training
• Semantic Leakage