Privacy Preserving Semantic Communications in Wireless Edge Networks with Vision Language Models

Abstract

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 m

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@misc{chang2026privacy,
  title = {{Privacy Preserving Semantic Communications in Wireless Edge Networks with Vision Language Models}},
  author = {Hao Chang and Ming-Zhe Chen and Qianqian Zhang},
  year = {2026},
  month = aug,
  eprint = {2608.21773},
  archivePrefix = {arXiv},
  url = {https://www.semanticscholar.org/paper/ac78104890d618aae3ff2db3509cf2c3ff30949b}
}