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arXiv · 2410.18418

Knowledge-Assisted Privacy Preserving in Semantic Communication

Xuesong Liu·Yao Sun·Runze Cheng·Le Xia·Hanaa Abumarshoud·Lei Zhang·Muhammad Ali Imran
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Abstract

Semantic communication (SC) offers promising advancements in data transmission efficiency and reliability by focusing on delivering true meaning rather than solely binary bits of messages. However, privacy concerns in SC might become outstanding. Eavesdroppers equipped with advanced semantic coding models and extensive knowledge could be capable of correctly decoding and reasoning sensitive semantics from just a few stolen bits. To this end, this article explores utilizing knowledge to enhance data privacy in SC networks. Specifically, we first identify the potential attacks in SC based on the analysis of knowledge. Then, we propose a knowledge-assisted privacy preserving SC framework, which consists of a data transmission layer for precisely encoding and decoding source messages, and a knowledge management layer responsible for injecting appropriate knowledge into the transmission pair. Moreover, we elaborate on the transceiver design in the proposed SC framework to explain how knowledge should be utilized properly. Finally, some challenges of the proposed SC framework are discussed to expedite the practical implementation.

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Xuesong Liu, Yao Sun, Runze Cheng, Le Xia, Hanaa Abumarshoud, Lei Zhang, Muhammad Ali Imran. 2024-10-24. Knowledge-Assisted Privacy Preserving in Semantic Communication. https://arxiv.org/abs/2410.18418

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