Secure Semantic Communication With Homomorphic Encryption
- URL: http://arxiv.org/abs/2501.10182v1
- Date: Fri, 17 Jan 2025 13:26:14 GMT
- Title: Secure Semantic Communication With Homomorphic Encryption
- Authors: Rui Meng, Dayu Fan, Haixiao Gao, Yifan Yuan, Bizhu Wang, Xiaodong Xu, Mengying Sun, Chen Dong, Xiaofeng Tao, Ping Zhang, Dusit Niyato,
- Abstract summary: This paper explores the feasibility of applying homomorphic encryption to SemCom.
We propose a task-oriented SemCom scheme secured through homomorphic encryption.
- Score: 52.5344514499035
- License:
- 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.
Related papers
- CipherGuard: Compiler-aided Mitigation against Ciphertext Side-channel Attacks [30.992038220253797]
CipherGuard is a compiler-aided mitigation methodology to counteract ciphertext side channels with high efficiency and security.
We demonstrate that CipherGuard can strengthen the security of various cryptographic implementations more efficiently than existing state-of-the-art defense mechanism, i.e., CipherFix.
arXiv Detail & Related papers (2025-02-19T03:22:36Z) - Multi-Layered Security System: Integrating Quantum Key Distribution with Classical Cryptography to Enhance Steganographic Security [0.0]
We present a novel cryptographic system that integrates Quantum Key Distribution (QKD) with classical encryption techniques.
Our approach leverages the E91 QKD protocol to generate a shared secret key between communicating parties.
This key is then hashed using the Secure Hash Algorithm (SHA) to provide a fixedlength, high-entropy key.
arXiv Detail & Related papers (2024-08-13T15:20:29Z) - Trustworthy Image Semantic Communication with GenAI: Explainablity, Controllability, and Efficiency [59.15544887307901]
Image semantic communication (ISC) has garnered significant attention for its potential to achieve high efficiency in visual content transmission.
Existing ISC systems based on joint source-channel coding face challenges in interpretability, operability, and compatibility.
We propose a novel trustworthy ISC framework that employs Generative Artificial Intelligence (GenAI) for multiple downstream inference tasks.
arXiv Detail & Related papers (2024-08-07T14:32:36Z) - Boosting Digital Safeguards: Blending Cryptography and Steganography [0.30783046172997025]
Steganography involves hiding data within another medium, thereby facilitating covert communication by making the message invisible.
This proposed approach takes advantage of the latest advancements in Artificial Intelligence (AI) and Deep Learning (DL), especially through the application of Generative Adversarial Networks (GANs)
The application of GANs enables a smart, secure system that utilizes the inherent sensitivity of neural networks to slight alterations in data.
arXiv Detail & Related papers (2024-04-09T03:36:39Z) - Coding-Based Hybrid Post-Quantum Cryptosystem for Non-Uniform Information [53.85237314348328]
We introduce for non-uniform messages a novel hybrid universal network coding cryptosystem (NU-HUNCC)
We show that NU-HUNCC is information-theoretic individually secured against an eavesdropper with access to any subset of the links.
arXiv Detail & Related papers (2024-02-13T12:12:39Z) - Grain-128PLE: Generic Physical-Layer Encryption for IoT Networks [6.515605001492591]
Grain-128PLE is a lightweight physical layer encryption scheme that is derived from the Grain-128AEAD v2 stream cipher.
The design of Grain-128PLE maintains the structure of the main building blocks of the original Grain-128AEAD v2 stream cipher.
arXiv Detail & Related papers (2023-09-27T10:48:52Z) - Generative AI-aided Joint Training-free Secure Semantic Communications
via Multi-modal Prompts [89.04751776308656]
This paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding.
In response to security concerns, we introduce the application of covert communications aided by a friendly jammer.
arXiv Detail & Related papers (2023-09-05T23:24:56Z) - SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems [51.97204522852634]
We present a unified framework that aims to secure an online semantic communications system with three semantic protection modules.
Specifically, these protection modules are able to encrypt semantics to be transmitted by an encryption method, mitigate privacy risks from wireless channels by a perturbation mechanism, and calibrate distorted semantics at the destination.
Our framework enables an existing online SC system to dynamically assemble the above three pluggable modules to meet customized semantic protection requirements.
arXiv Detail & Related papers (2023-09-04T06:34:43Z) - Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial
Attacks [70.51799606279883]
We introduce test-time adversarial attacks on deep neural networks (DNNs) for semantic communications.
We show that it is possible to change the semantics of the transferred information even when the reconstruction loss remains low.
arXiv Detail & Related papers (2022-12-20T17:13:22Z) - Verifiable Encodings for Secure Homomorphic Analytics [10.402772462535884]
Homomorphic encryption is a promising solution for protecting privacy of cloud-delegated computations on sensitive data.
We propose two error detection encodings and build authenticators that enable practical client-verification of cloud-based homomorphic computations.
We implement our solution in VERITAS, a ready-to-use system for verification of outsourced computations executed over encrypted data.
arXiv Detail & Related papers (2022-07-28T13:22:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.