Secure Semantic Communication With Homomorphic Encryption

2025/01/17 by Rui Meng, Meng, Rui, Fan, Dayu +17 · 9 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Cooperative Communication and Network Coding #Cryptography and Data Security #Cryptography and Security (cs.CR) #DNA and Biological Computing #FOS: Computer and information sciences #FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2501.10182

openalex publication_date 2025/01/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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, existing encryption schemes encounter obstacles when applied to SemCom. To address this issue, this paper explores the feasibility of applying homomorphic encryption (HE) 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 overview HE techniques and employ scale-invariant feature transform (SIFT) to demonstrate that the extractable semantic information can be preserved in homomorphic encrypted ciphertext. Based on this finding, we further propose the HE-joint source-channel coding (HE-JSCC) scheme, where the traditional JSCC model architecture is modified to support HE operations. Moreover, we present the simulation results for image classification and image generation tasks. Furthermore, we provide potential future research directions for homomorphic encrypted SemCom.

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• SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attacks
• A DRL-Empowered Multi-Level Jamming Approach for Secure Semantic Communication
• Privacy-Preserving Semantic Communication over Wiretap Channels with Learnable Differential Privacy
• SemSteDiff: Generative Diffusion Model-based Coverless Semantic Steganography Communication
• SREC: Encrypted Semantic Super-Resolution Enhanced Communication
• Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art
• Secure and Efficient UAV-Based Face Detection via Homomorphic Encryption and Edge Computing

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