用差分隐私保护语义通信中的敏感信息，防止窃听者还原原始数据。
Enhancing Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy
- 通过GAN反演提取解耦的语义特征，结合神经网络实现差分隐私的加解密。
- 在不同隐私预算和信道条件下，仍能保持高质量图像重建。
- 生成混乱或虚假图像干扰窃听者，适合高隐私要求的通信场景。
语义通信（SemCom）通过聚焦任务相关的信息提升传输效率，但在不安全信道上传输富含语义的数据会带来隐私风险。本文提出一种融合差分隐私（DP）机制的新型语义通信框架。该方法利用生成对抗网络（GAN）反演技术提取解耦的语义特征，并采用神经网络（NNs）近似实现差分隐私的添加与移除过程，有效缓解了差分隐私带来的不可逆问题。此外，引入基于神经网络的加密方案以增强信道输入的安全性。仿真结果表明，所提方法可有效防止窃听者重构敏感信息，生成混沌或虚假图像干扰攻击者，同时保障合法用户端的高质量图像重建。系统在多种隐私预算与信道条件下均表现稳健，实现了隐私保护与重建保真度之间的最优平衡。
原文摘要 · Abstract (English)
Semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information. However, transmitting semantic-rich data over insecure channels introduces privacy risks. This paper proposes a novel SemCom framework that integrates differential privacy (DP) mechanisms to protect sensitive semantic features. This method employs the generative adversarial network (GAN) inversion technique to extract disentangled semantic features and uses neural networks (NNs) to approximate the DP application and removal processes, effectively mitigating the non-invertibility issue of DP. Additionally, an NN-based encryption scheme is introduced to strengthen the security of channel inputs. Simulation results demonstrate that the proposed approach effectively prevents eavesdroppers from reconstructing sensitive information by generating chaotic or fake images, while ensuring high-quality image reconstruction for legitimate users. The system exhibits robust performance across various privacy budgets and channel conditions, achieving an optimal balance between privacy protection and reconstruction fidelity.
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