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Semantic communication is implemented based on shared background knowledge, but the sharing mechanism risks privacy leakage. In this letter, we propose an encrypted semantic communication system (ESCS) for privacy preserving, which combines universality and confidentiality. The universality is reflected in that all network modules of the proposed ESCS are trained based on a shared database, which is suitable for large-scale deployment in practical scenarios. Meanwhile, the confidentiality is achieved by symmetric encryption. Based on the adversarial training, we design an adversarial encryption training scheme to guarantee the accuracy of semantic communication in both encrypted and unencrypted modes. Experiment results show that the proposed ESCS with the adversarial encryption training scheme can perform well regardless of whether the semantic information is encrypted. It is difficult for the attacker to reconstruct the original semantic information from the eavesdropped message.

In this letter, we propose a semantic communication scheme for wireless relay channels based on Autoencoder, named AESC, which encodes and decodes sentences from the semantic dimension. The Autoencoder module provides antinoise performance for the system. Meanwhile, a novel semantic forward (SF) mode is designed for the relay node to forward the semantic information at the semantic level, especially for the scenarios that there is no common knowledge shared between the source and destination nodes. Numerical results show that the AESC achieves better stability performance than the traditional communication schemes, and the proposed SF mode provides a significant performance gain compared to the traditional forward protocols.

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