AI 中文总结
该研究针对无线边缘网络语义通信的隐私泄露问题，提出基于VLM的隐私保护语义通信框架，通过私有区域移除、加密收发器和语义信息瓶颈实现隐私保护，同时保证重构质量并抑制跨设备冗余。
AI 中文摘要
语义通信作为下一代无线系统的有前景范式，通过传输高级语义特征而非原始比特实现通信，但协作设备与多模态传输会增加隐私风险，因为敏感信息可能通过设备间语义融合和跨模态表示泄露。为解决该问题，我们提出一种用于无线边缘网络的隐私保护语义通信框架，该框架利用视觉语言模型（VLM）从图像中提取文本语义，并仅通过边缘服务器维护的隐私数据库识别隐私敏感实体；在图像传输前，每个设备会移除已识别的私有区域，同时保留有用语义内容，服务器则利用文本嵌入和基于VLM的语义先验从接收的掩码图像中重构被移除区域。为保护文本信息，我们设计了一种无需预共享密钥、利用互易无线信道生成的物理层密钥的加密语义信道收发器，还引入语义信息瓶颈以抑制多设备间的冗余信息。该框架针对强大的模型感知对手进行评估，该对手可拦截无线传输并访问边缘设备模型参数，但无法访问服务器端数据；仿真结果表明，与无隐私保护的语义通信方案相比，所提方法将隐私泄露降低了50%以上，而授权服务器的感知重构质量较对手提升了48%，传输表示间的估计互信息接近0比特，表明跨设备语义冗余得到有效抑制。
英文摘要
Semantic communication has emerged as a promising paradigm for next-generation wireless systems by transmitting high-level semantic features rather than raw bits. However, collaborative devices and multimodal transmission increase privacy risks because sensitive information may leak through inter-device semantic fusion and cross-modal representations. To address this issue, we propose a privacy-preserving semantic communication framework for wireless edge networks. Leveraging a vision-language model (VLM), the framework extracts textual semantics from images and identifies privacy-sensitive entities using a privacy database maintained only at the edge server. Before image transmission, each device removes the identified private regions while preserving useful semantic content. The server then reconstructs the removed regions from the received masked images using textual embeddings and VLM-based semantic priors. To protect textual information, we design an encrypted semantic-channel transceiver using physical-layer keys generated from reciprocal wireless channels, without pre-shared keys. We also introduce a semantic information bottleneck to suppress redundant information across multiple devices. The framework is evaluated against a strong model-aware adversary that can intercept wireless transmissions and access edge-device model parameters but not server-side data. Simulation results show that the proposed method reduces privacy leakage by more than 50% compared with a semantic communication scheme without privacy protection, while the authorized server achieves a 48% improvement in perceptual reconstruction quality over the adversary. The estimated mutual information between transmitted representations approaches 0 bit, indicating effective suppression of cross-device semantic redundancy.
