Secure Semantic Communication With Homomorphic Encryption (2501.10182v1)

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.

• The paper critiques traditional encryption methods for Semantic Communication (SemCom) for disrupting semantic relationships and limiting operations, advocating for a more suitable solution.
• It proposes Homomorphic Encryption (HE) as a solution for SemCom security, enabling operations on encrypted data to preserve semantic features, validated by methods like SIFT.
• The research presents a novel secure SemCom scheme integrating deep JSCC with HE, demonstrating comparable classification accuracy on encrypted images and improved performance in low Signal-to-Noise Ratio environments.

Comprehensive Overview of Secure Semantic Communication with Homomorphic Encryption

The paper "Secure Semantic Communication With Homomorphic Encryption" by Rui Meng et al. explores an innovative intersection of communication and security, particularly focusing on Semantic Communication (SemCom) systems and their security under homomorphic encryption. SemCom, integral to the next generation of wireless communication systems, offers a paradigm shift from traditional communication by focusing on the transmission of meaningful information rather than mere data packets. The complex landscape of 6G is thoroughly explained, emphasizing the embedded intelligence and the need for secure transmission mechanisms.

Core Contributions

1. Challenges of Traditional Encryption in SemCom: The authors provide a detailed review of existing encryption algorithms from 1G to 5G, such as KASUMI and AES, and elucidate their limitations in the context of SemCom. Traditional cryptosystems often disrupt semantic relationships, suffer from the avalanche effect, and restrict operational diversity, which means they fall short in addressing the nuanced security needs of SemCom.
2. Homomorphic Encryption as a Solution: Arguing for the appropriateness of homomorphic encryption in SemCom, the paper demonstrates that operations can be conducted on encrypted data without prior decryption, thereby preserving semantic features. This enhances computation without compromising confidentiality. The use of Scale-Invariant Feature Transform (SIFT) to validate semantic retention within the ciphertext affirms this approach's feasibility.
3. Design and Evaluation of a Secure SemCom Scheme: The paper proposes a novel SemCom scheme integrating a privacy-preserved deep Joint Source-Channel Coding (JSCC) encoder and decoder under homomorphic encryption. Notably, the scheme achieves comparable classification accuracy with homomorphic ciphertext images as plaintext images. This robust scheme is rigorously evaluated against traditional models, emphasizing improved accuracy and resilient performance in low Signal-to-Noise Ratio (SNR) environments.
4. Future Directions: Potential enhancements include selective encryption based on semantic importance, leveraging adversarial training for improved generalization, optimizing the computational efficiency of homomorphic encryption, and deploying advanced computing resources like GPUs and FPGAs.

Theoretical and Practical Implications

The authors highlight significant theoretical advancements, notably the integration of homomorphic encryption into a deep learning-based communication framework. This integration paves the way for more secure and efficient data handling in SemCom systems, a necessity for evolving 6G technologies. Practically, the research introduces methodologies for optimizing communication systems for better privacy protection while maintaining data usability and system performance.

The paper's exploration of deep JSCC models supporting homomorphic operations is pertinent for efficiently compressing semantic data without the loss of meaningful information. By transforming conventional security measures, the authors exemplify how secure communication can underpin future networks, essential for applications like smart cities and autonomous vehicles.

Conclusion

In sum, Rui Meng et al.’s paper provides an insightful, technical dissection of SemCom systems under the lens of modern cryptography. By addressing the challenges of traditional encryption and proposing a feasible solution via homomorphic encryption, the work significantly contributes to enhancing security paradigms within next-generation communication frameworks. The combination of empirical evidence and theoretical exploration sets a solid foundation for future research and operational deployment in secure, efficient communication systems within the rapidly evolving telecommunications landscape.