  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.
