Theoretically-Grounded Privacy-Preserving Deep Semantic Coding
Zuxing Li, Nishan Wu, Qi Jiang, Yifeng Chen, Nguyen Huu Trung · 2025
As an emerging communication paradigm, semantic communication can significantly improve transmission efficiency by focusing on extracting and preserving task-critical semantic information. However, privacy preservation of sensitive information in semantic communication has not been well studied. This letter focuses on the semantic coding and studies the fundamental trade-off among data compression, source distortion, semantic distortion, and privacy preservation from an information-theoretic perspective. Based on the theoretic results, a novel deep learning method is proposed for data-driven privacy-preserving semantic coding design. The theory and method are validated through experiments on the MNIST dataset.