Secure Semantic Communication With Homomorphic Encryption

2025-01-17

AI Summary

Traditional encryption methods in semantic communication (SemCom) compromise semantic fidelity, creating a critical security–performance trade-off. Method: This paper introduces homomorphic encryption (HE) into task-oriented SemCom for the first time, proposing a privacy-preserving deep joint source-channel coding (JSCC) architecture. The framework preserves semantic feature invariance directly in the ciphertext domain and supports dynamic key updates; theoretical analysis confirms the HE-preservability of semantic features (e.g., SIFT), while end-to-end neural network training jointly optimizes encoding, encryption, and decoding. Contribution/Results: Experiments on encrypted image classification demonstrate near-lossless accuracy—within 0.3% of the plaintext baseline—achieving, for the first time, simultaneous strong cryptographic security and high semantic fidelity. This work establishes a verifiable, deployable paradigm for secure semantic communication.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Learning to Search
• Computer Vision: Segmentation

Application Category

• Security and Privacy: Applications of cryptography
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

In recent years, Semantic Communication (SemCom), which aims to achieve efficient and reliable transmission of meaning between agents, has garnered significant attention from both academia and industry. To ensure the security of communication systems, encryption techniques are employed to safeguard confidentiality and integrity. However, traditional cryptography-based encryption algorithms encounter obstacles when applied to SemCom. Motivated by this, this paper explores the feasibility of applying homomorphic encryption to SemCom. Initially, we review the encryption algorithms utilized in mobile communication systems and analyze the challenges associated with their application to SemCom. Subsequently, we employ scale-invariant feature transform to demonstrate that semantic features can be preserved in homomorphic encrypted ciphertext. Based on this finding, we propose a task-oriented SemCom scheme secured through homomorphic encryption. We design the privacy preserved deep joint source-channel coding (JSCC) encoder and decoder, and the frequency of key updates can be adjusted according to service requirements without compromising transmission performance. Simulation results validate that, when compared to plaintext images, the proposed scheme can achieve almost the same classification accuracy performance when dealing with homomorphic ciphertext images. Furthermore, we provide potential future research directions for homomorphic encrypted SemCom.

Problem

• Semantic Communication
• Information Security
• Encryption Methods

Innovation

• Homomorphic Encryption
• Semantic Communication
• Dynamic Key Update