基于对抗训练的加密语义通信隐私保护
Encrypted Semantic Communication Using Adversarial Training for Privacy Preserving
摘要：
语义通信是基于共享的背景知识来实现的，但是共享机制存在隐私泄露的风险。在这封信中，我们提出了一个加密的语义通信系统（ESCS）的隐私保护，它结合了通用性和机密性。通用性体现在所提出的ESCS的所有网络模块都基于共享数据库进行训练，这适合于实际场景中的大规模部署。同时，通过对称加密来实现机密性。在对抗训练的基础上，设计了一种对抗加密训练方案，以保证加密和非加密模式下语义通信的准确性。实验结果表明，无论语义信息是否加密，采用对抗加密训练方案的ESCS都能表现良好。攻击者很难从被窃听的消息中重建出原始的语义信息。
英文摘要：
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.
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