A Secure Semantic Communication System Based on Knowledge Graph

2025-11-17

AI Summary

To address security vulnerabilities in text transmission within semantic communication, this paper proposes an end-to-end secure semantic communication framework integrating knowledge graphs with multi-layer encryption. Methodologically, it first constructs a domain-specific knowledge graph to enable structured semantic representation; second, it designs a joint physical-layer encryption scheme combining constellation diagonal transformation and multi-parameter weighted fractional Fourier transform (MP-WFRFT); finally, it employs a Transformer-based decoder to achieve high-fidelity reconstruction of encrypted semantic representations. Experiments demonstrate that the legitimate receiver achieves a BLEU score of 0.90, while eavesdroppers attain less than 0.30—yielding up to a 20% improvement in eavesdropping resistance over baseline methods. The core contribution lies in the deep integration of knowledge-graph-driven semantic modeling with reversible, nonlinear dual-domain encryption, thereby jointly ensuring semantic fidelity and channel-level security.

Technology Category

• Natural Language Processing: Safety and Robustness
• Computer Vision: Multi-modal Vision
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Applications of cryptography
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

This study proposes a novel approach to ensure the security of textual data transmission in a semantic communication system. In the proposed system, a sender transmits textual information to a receiver, while a potential eavesdropper attempts to intercept the information. At the sender side, the text is initially preprocessed, where each sentence is annotated with its corresponding topic, and subsequently extracted into a knowledge graph. To achieve the secure transmission of the knowledge graph, we propose a channel encryption scheme that integrates constellation diagonal transformation with multi-parameter weighted fractional Fourier transform (MP-WFRFT). At the receiver side, the textual data is first decrypted, and then recovered via a transformer model. Experimental results demonstrate that the proposed method reduces the probability of information compromise. The legitimate receiver achieves a Bilingual Evaluation Understudy (BLEU) score of 0.9, whereas the BLEU score of the eavesdropper remains below 0.3. Compared to the baselines, the proposed method can improve the security by up to 20%.

Problem

• Securing textual data transmission against eavesdroppers in semantic communication
• Protecting knowledge graph transmission using constellation diagonal transformation encryption
• Ensuring legitimate receiver comprehension while preventing eavesdropper understanding

Innovation

• Text preprocessing with topic annotation and knowledge graph extraction
• Channel encryption using constellation diagonal transformation and MP-WFRFT
• Transformer model for decryption and text recovery at receiver