Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Survey on Semantic Communication Networks: Architecture, Security, and Privacy (2405.01221v1)

Published 2 May 2024 in cs.NI

Abstract: Semantic communication, emerging as a breakthrough beyond the classical Shannon paradigm, aims to convey the essential meaning of source data rather than merely focusing on precise yet content-agnostic bit transmission. By interconnecting diverse intelligent agents (e.g., autonomous vehicles and VR devices) via semantic communications, the semantic communication networks (SemComNet) supports semantic-oriented transmission, efficient spectrum utilization, and flexible networking among collaborative agents. Consequently, SemComNet stands out for enabling ever-increasing intelligent applications, such as autonomous driving and Metaverse. However, being built on a variety of cutting-edge technologies including AI and knowledge graphs, SemComNet introduces diverse brand-new and unexpected threats, which pose obstacles to its widespread development. Besides, due to the intrinsic characteristics of SemComNet in terms of heterogeneous components, autonomous intelligence, and large-scale structure, a series of critical challenges emerge in securing SemComNet. In this paper, we provide a comprehensive and up-to-date survey of SemComNet from its fundamentals, security, and privacy aspects. Specifically, we first introduce a novel three-layer architecture of SemComNet for multi-agent interaction, which comprises the control layer, semantic transmission layer, and cognitive sensing layer. Then, we discuss its working modes and enabling technologies. Afterward, based on the layered architecture of SemComNet, we outline a taxonomy of security and privacy threats, while discussing state-of-the-art defense approaches. Finally, we present future research directions, clarifying the path toward building intelligent, robust, and green SemComNet. To our knowledge, this survey is the first to comprehensively cover the fundamentals of SemComNet, alongside a detailed analysis of its security and privacy issues.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (173)
  1. P. Zhang, H. Yang, Z. Feng, Y. Cui, J. Dai, X. Qin, J. Li, and Q. Zhang, “Toward intelligent and efficient 6g networks: Jcsc enabled on-purpose machine communications,” IEEE Wireless Communications, vol. 30, no. 1, pp. 150–157, 2022.
  2. X. Luo, H.-H. Chen, and Q. Guo, “Semantic Communications: Overview, Open Issues, and Future Research Directions,” IEEE Wireless Communications, vol. 29, no. 1, pp. 210–219, 2022.
  3. P. Zhang, W. Xu, H. Gao, K. Niu, X. Xu, X. Qin, C. Yuan, Z. Qin, H. Zhao, J. Wei, and F. Zhang, “Toward Wisdom-Evolutionary and Primitive-Concise 6G: A New Paradigm of Semantic Communication Networks,” Engineering, vol. 8, pp. 60–73, 2022.
  4. C. Li, L. Zeng, X. Huang, X. Miao, and S. Wang, “Secure Semantic Communication Model for Black-Box Attack Challenge Under Metaverse,” IEEE Wireless Communications, vol. 30, no. 4, pp. 56–62, 2023.
  5. J. Kang, J. He, H. Du, Z. Xiong, Z. Yang, X. Huang, and S. Xie, “Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses,” IEEE Wireless Communications, vol. 30, no. 4, pp. 48–55, 2023.
  6. L. Xia, Y. Sun, C. Liang, D. Feng, R. Cheng, Y. Yang, and M. A. Imran, “WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery,” IEEE Wireless Communications, vol. 30, no. 2, pp. 32–39, 2023.
  7. G. Shi, Y. Xiao, Y. Li, and X. Xie, “From Semantic Communication to Semantic-Aware Networking: Model, Architecture, and Open Problems,” IEEE Communications Magazine, vol. 59, no. 8, pp. 44–50, 2021.
  8. E. Uysal, O. Kaya, A. Ephremides, J. Gross, M. Codreanu, P. Popovski, M. Assaad, G. Liva, A. Munari, B. Soret, T. Soleymani, and K. H. Johansson, “Semantic communications in networked systems: A data significance perspective,” IEEE Network, vol. 36, no. 4, pp. 233–240, 2022.
  9. H. Xie, Z. Qin, G. Y. Li, and B.-H. Juang, “Deep Learning Enabled Semantic Communication Systems,” IEEE Transactions on Signal Processing, vol. 69, pp. 2663–2675, 2021.
  10. G. Zhang, Q. Hu, Z. Qin, Y. Cai, G. Yu, and X. Tao, “A unified multi-task semantic communication system for multimodal data,” IEEE Transactions on Communications, 2024, doi: 10.1109/TCOMM.2024.3364990.
  11. Z. Lu, R. Li, K. Lu, X. Chen, E. Hossain, Z. Zhao, and H. Zhang, “Semantics-Empowered Communications: A Tutorial-cum-Survey,” IEEE Communications Surveys & Tutorials, vol. 26, no. 1, pp. 41–79, 2024.
  12. H. Zhang, H. Wang, Y. Li, K. Long, and A. Nallanathan, “DRL-Driven Dynamic Resource Allocation for Task-Oriented Semantic Communication,” IEEE Transactions on Communications, vol. 71, no. 7, pp. 3992–4004, 2023.
  13. Z. Qin, X. Tao, J. Lu, W. Tong, and G. Y. Li, “Semantic Communications: Principles and Challenges,” arXiv preprint arXiv:2201.01389, 2022.
  14. Q. Wu, G. Ding, Y. Xu, S. Feng, Z. Du, J. Wang, and K. Long, “Cognitive Internet of Things: A New Paradigm Beyond Connection,” IEEE Internet of Things Journal, vol. 1, no. 2, pp. 129–143, 2014.
  15. T. Han, Q. Yang, Z. Shi, S. He, and Z. Zhang, “Semantic-Preserved Communication System for Highly Efficient Speech Transmission,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 245–259, 2023.
  16. B. Wang, R. Li, J. Zhu, Z. Zhao, and H. Zhang, “Knowledge Enhanced Semantic Communication Receiver,” IEEE Communications Letters, vol. 27, no. 7, pp. 1794–1798, 2023.
  17. T. Han, Q. Yang, Z. Shi, S. He, and Z. Zhang, “Semantic-preserved communication system for highly efficient speech transmission,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 245–259, 2023.
  18. K. Huang, Q. Lan, Z. Liu, and L. Yang, “Semantic Data Sourcing for 6G Edge Intelligence,” IEEE Communications Magazine, pp. 1–7, 2023.
  19. X. Peng, Z. Qin, D. Huang, X. Tao, J. Lu, G. Liu, and C. Pan, “A Robust Deep Learning Enabled Semantic Communication System for Text,” in Proceedings of IEEE Global Communications Conference (GLOBECOM), 2022, pp. 2704–2709.
  20. M. Shen, J. Wang, H. Du, D. Niyato, X. Tang, J. Kang, Y. Ding, and L. Zhu, “Secure Semantic Communications: Challenges, Approaches, and Opportunities,” IEEE Network, 2023, doi: 10.1109/MNET.2023.3327111.
  21. I. Shumailov, Y. Zhao, D. Bates, N. Papernot, R. Mullins, and R. Anderson, “Sponge Examples: Energy-Latency Attacks on Neural Networks,” in Proceedings of IEEE European Symposium on Security and Privacy (EuroS&P), 2021, pp. 212–231.
  22. H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, M. Guizani, and D. I. Kim, “Rethinking Wireless Communication Security in Semantic Internet of Things,” IEEE Wireless Communications, vol. 30, no. 3, pp. 36–43, 2023.
  23. A. Li, X. Wei, D. Wu, and L. Zhou, “Cross-Modal Semantic Communications,” IEEE Wireless Communications, vol. 29, no. 6, pp. 1–8, 2022.
  24. Y. Wang, S. Guo, Y. Deng, H. Zhang, and Y. Fang, “Privacy-preserving task-oriented semantic communications against model inversion attacks,” IEEE Transactions on Wireless Communications, 2024, doi: 10.1109/TWC.2024.3369170.
  25. G. Nan, Z. Li, J. Zhai, Q. Cui, G. Chen, X. Du, X. Zhang, X. Tao, Z. Han, and T. Q. S. Quek, “Physical-Layer Adversarial Robustness for Deep Learning-Based Semantic Communications,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 8, pp. 2592–2608, 2023.
  26. Y. Wang, Z. Su, S. Guo, M. Dai, T. H. Luan, and Y. Liu, “A Survey on Digital Twins: Architecture, Enabling Technologies, Security and Privacy, and Future Prospects,” IEEE Internet of Things Journal, vol. 10, no. 17, pp. 14 965–14 987, 2023.
  27. S. Cheng, X. Zhang, Y. Sun, Q. Cui, and X. Tao, “Knowledge discrepancy oriented privacy preserving for semantic communication,” IEEE Transactions on Vehicular Technology, pp. 1–10, 2024, doi: 10.1109/TVT.2024.3381222.
  28. T. Qiu, N. Chen, K. Li, M. Atiquzzaman, and W. Zhao, “How Can Heterogeneous Internet of Things Build Our Future: A Survey,” IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 2011–2027, 2018.
  29. Z. Tian, H. Vo, C. Zhang, G. Min, and S. Yu, “An Asynchronous Multi-Task Semantic Communication Method,” IEEE Network, 2023, doi: 10.1109/MNET.2023.3321547.
  30. Q. Lan, D. Wen, Z. Zhang, Q. Zeng, X. Chen, P. Popovski, and K. Huang, “What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence,” Journal of Communications and Information Networks, vol. 6, no. 4, pp. 336–371, 2021.
  31. D. Gündüz, Z. Qin, I. E. Aguerri, H. S. Dhillon, Z. Yang, A. Yener, K. K. Wong, and C.-B. Chae, “Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 5–41, 2023.
  32. W. Yang, H. Du, Z. Q. Liew, W. Y. B. Lim, Z. Xiong, D. Niyato, X. Chi, X. Shen, and C. Miao, “Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges,” IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 213–250, 2023.
  33. Z. Qin, F. Gao, B. Lin, X. Tao, G. Liu, and C. Pan, “A Generalized Semantic Communication System: From Sources to Channels,” IEEE Wireless Communications, vol. 30, no. 3, pp. 18–26, 2023.
  34. A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.
  35. M. Xu, H. Du, D. Niyato, J. Kang, Z. Xiong, S. Mao, Z. Han, A. Jamalipour, D. I. Kim, X. Shen, V. C. M. Leung, and H. V. Poor, “Unleashing the power of edge-cloud generative AI in mobile networks: A survey of aigc services,” IEEE Communications Surveys & Tutorials, 2024, doi: 10.1109/COMST.2024.3353265.
  36. B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz, and D. Roth, “Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey,” ACM Computing Surveys, vol. 56, no. 2, pp. 1–40, 2023.
  37. Y. Xiao, Z. Sun, G. Shi, and D. Niyato, “Imitation Learning-Based Implicit Semantic-Aware Communication Networks: Multi-Layer Representation and Collaborative Reasoning,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 3, pp. 639–658, 2023.
  38. H. Xie, Z. Qin, X. Tao, and K. B. Letaief, “Task-Oriented Multi-User Semantic Communications,” IEEE Journal on Selected Areas in Communications, vol. 40, no. 9, pp. 2584–2597, 2022.
  39. X. Luo, R. Gao, H.-H. Chen, S. Chen, Q. Guo, and P. N. Suganthan, “Multi-Modal and Multi-User Semantic Communications for Channel-Level Information Fusion,” IEEE Wireless Communications, pp. 1–18, 2022.
  40. E. Bourtsoulatze, D. B. Kurka, and D. Gunduz, “Deep Joint Source-Channel Coding for Wireless Image Transmission,” IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 3, pp. 567–579, 2019.
  41. J. Xu, B. Ai, W. Chen, N. Wang, and M. Rodrigues, “Deep joint source-channel coding for image transmission with visual protection,” IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 6, pp. 1399–1411, 2023.
  42. L. Xia, Y. Sun, C. Liang, L. Zhang, M. A. Imran, and D. Niyato, “Generative AI for Semantic Communication: Architecture, Challenges, and Outlook,” arXiv preprint arXiv:2308.15483, 2023.
  43. H. Seo, J. Park, M. Bennis, and M. Debbah, “Semantics-Native Communication via Contextual Reasoning,” IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 3, pp. 604–617, 2023.
  44. Q. Zhao, G. Li, J. Cai, M. Zhou, and L. Feng, “A Tutorial on Internet of Behaviors: Concept, Architecture, Technology, Applications, and Challenges,” IEEE Communications Surveys & Tutorials, vol. 25, no. 2, pp. 1227–1260, 2023.
  45. D. D. Bourgin, J. C. Peterson, D. Reichman, S. J. Russell, and T. L. Griffiths, “Cognitive model priors for predicting human decisions,” in Proceedings of International Conference on Machine Learning (ICML), 2019, pp. 5133–5141.
  46. Q. Wu, T. Ruan, F. Zhou, Y. Huang, F. Xu, S. Zhao, Y. Liu, and X. Huang, “A Unified Cognitive Learning Framework for Adapting to Dynamic Environments and Tasks,” IEEE Wireless Communications, vol. 28, no. 6, pp. 208–216, 2021.
  47. N. Rabinowitz, F. Perbet, F. Song, C. Zhang, S. M. A. Eslami, and M. Botvinick, “Machine Theory of Mind,” in Proceedings of International Conference on Machine Learning (ICML), 2018, pp. 4218–4227.
  48. H. Zhang, S. Shao, M. Tao, X. Bi, and K. B. Letaief, “Deep Learning-Enabled Semantic Communication Systems With Task-Unaware Transmitter and Dynamic Data,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 170–185, 2023.
  49. A. A. Abbasi and M. Younis, “A survey on clustering algorithms for wireless sensor networks,” Computer Communications, vol. 30, no. 14, pp. 2826–2841, 2007.
  50. J. Guo, H. Chen, B. Song, Y. Chi, C. Yuen, F. R. Yu, G. Y. Li, and D. Niyato, “Distributed task-oriented communication networks with multimodal semantic relay and edge intelligence,” IEEE Communications Magazine, 2024, doi: 10.1109/MCOM.001.2300155.
  51. B. Tang, L. Huang, Q. Li, A. Pandharipande, and X. Ge, “Cooperative Semantic Communication With On-Demand Semantic Forwarding,” IEEE Open Journal of the Communications Society, vol. 5, pp. 349–363, 2024.
  52. X. Luo, B. Yin, Z. Chen, B. Xia, and J. Wang, “Autoencoder-based Semantic Communication Systems with Relay Channels,” in Proceedings of IEEE International Conference on Communications Workshops (ICC WKSHPS), 2022, pp. 711–716.
  53. W. Lin, Y. Yan, L. Li, Z. Han, and T. Matsumoto, “Semantic-forward relaying: A novel framework towards 6g cooperative communications,” IEEE Communications Letters, 2024, doi: 10.1109/LCOMM.2024.3352916.
  54. M. De Lange, R. Aljundi, M. Masana, S. Parisot, X. Jia, A. Leonardis, G. Slabaugh, and T. Tuytelaars, “A Continual Learning Survey: Defying Forgetting in Classification Tasks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3366–3385, 2022.
  55. Z. Yin, N. Cheng, Y. Hui, W. Wang, L. Zhao, K. Aldubaikhy, and A. Alqasir, “Multi-domain Resource Multiplexing Based Secure Transmission for Satellite-Assisted IoT: AO-SCA Approach,” IEEE Transactions on Wireless Communications, vol. 22, no. 11, pp. 7319–7330, 2023.
  56. W. Zhang, Y. Wang, M. Chen, T. Luo, and D. Niyato, “Optimization of Image Transmission in a Cooperative Semantic Communication Networks,” IEEE Transactions on Wireless Communications, 2023, doi: 10.1109/TWC.2023.3282906.
  57. Z. Fu, F. Huang, K. Ren, J. Weng, and C. Wang, “Privacy-Preserving Smart Semantic Search Based on Conceptual Graphs Over Encrypted Outsourced Data,” IEEE Transactions on Information Forensics and Security, vol. 12, no. 8, pp. 1874–1884, 2017.
  58. Y. Yao, J. Duan, K. Xu, Y. Cai, Z. Sun, and Y. Zhang, “A survey on large language model (llm) security and privacy: The good, the bad, and the ugly,” High-Confidence Computing, p. 100211, 2024.
  59. M. Mitev, A. Chorti, H. V. Poor, and G. P. Fettweis, “What Physical Layer Security Can Do for 6G Security,” IEEE Open Journal of Vehicular Technology, vol. 4, pp. 375–388, 2023.
  60. Z. Tian, L. Cui, J. Liang, and S. Yu, “A Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning,” ACM Computing Surveys, vol. 55, no. 8, pp. 1–35, 2022.
  61. J. Zhang, Z. Gu, J. Jang, H. Wu, M. P. Stoecklin, H. Huang, and I. Molloy, “Protecting Intellectual Property of Deep Neural Networks with Watermarking,” in Proceedings of Asia Conference on Computer and Communications Security (AsiaCCS), 2018, pp. 159–172.
  62. H. Nie, S. Lu, J. Wu, and J. Zhu, “Deep model intellectual property protection with compression-resistant model watermarking,” IEEE Transactions on Artificial Intelligence, 2024, doi: 10.1109/TAI.2024.3351116.
  63. Z. Xi, T. Du, C. Li, R. Pang, S. Ji, X. Luo, X. Xiao, F. Ma, and T. Wang, “On the Security Risks of Knowledge Graph Reasoning,” arXiv preprint arXiv:2305.02383, 2023.
  64. H. Zhang, H. Wang, Y. Li, K. Long, and V. C. M. Leung, “Toward Intelligent Resource Allocation on Task-Oriented Semantic Communication,” IEEE Wireless Communications, vol. 30, no. 3, pp. 70–77, 2023.
  65. Z. Wang, H. Guo, Z. Zhang, W. Liu, Z. Qin, and K. Ren, “Feature Importance-aware Transferable Adversarial Attacks,” in Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 7619–7628.
  66. J. Zhang, D. Chen, J. Liao, W. Zhang, H. Feng, G. Hua, and N. Yu, “Deep Model Intellectual Property Protection via Deep Watermarking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 8, pp. 4005–4020, 2022.
  67. N. B. Somy, K. Kannan, V. Arya, S. Hans, A. Singh, P. Lohia, and S. Mehta, “Ownership preserving AI market places using blockchain,” in Proceedings of IEEE international conference on blockchain (Blockchain).   IEEE, 2019, pp. 156–165.
  68. L. Xiao, W. Huang, Y. Xie, W. Xiao, and K.-C. Li, “A Blockchain-Based Traceable IP Copyright Protection Algorithm,” IEEE Access, vol. 8, pp. 49 532–49 542, 2020.
  69. Y. Li, J. Yang, Y. Song, L. Cao, J. Luo, and L.-J. Li, “Learning from Noisy Labels with Distillation,” in Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1928–1936.
  70. B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao, “Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks,” in Proceedings of IEEE Symposium on Security and Privacy (SP), 2019, pp. 707–723.
  71. P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, “Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent,” in Proceedings of Advances in Neural Information Processing Systems (NeurIPS), vol. 30, 2017.
  72. M. S. Ozdayi, M. Kantarcioglu, and Y. R. Gel, “Defending against Backdoors in Federated Learning with Robust Learning Rate,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 35, 2021, pp. 9268–9276.
  73. L. Zhao, S. Hu, Q. Wang, J. Jiang, C. Shen, X. Luo, and P. Hu, “Shielding Collaborative Learning: Mitigating Poisoning Attacks Through Client-Side Detection,” IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 5, pp. 2029–2041, 2021.
  74. J. Zhang and C. Li, “Adversarial Examples: Opportunities and Challenges,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 7, pp. 2578–2593, 2019.
  75. Q. Hu, G. Zhang, Z. Qin, Y. Cai, G. Yu, and G. Y. Li, “Robust Semantic Communications with Masked VQ-VAE Enabled Codebook,” IEEE Transactions on Wireless Communications, pp. 8707–8722, 2023.
  76. K. Li, B. P. L. Lau, X. Yuan, W. Ni, M. Guizani, and C. Yuen, “Toward Ubiquitous Semantic Metaverse: Challenges, Approaches, and Opportunities,” IEEE Internet of Things Journal, vol. 10, no. 24, pp. 21 855–21 872, 2023.
  77. Y. Wang, Y. Hu, H. Du, T. Luo, and D. Niyato, “Multi-Agent Reinforcement Learning for Covert Semantic Communications over Wireless Networks,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, pp. 1–5.
  78. N. Xie, J. Zhang, and Q. Zhang, “Security Provided by the Physical Layer in Wireless Communications,” IEEE Network, 2023.
  79. R. Yamaguchi, H. Ochiai, and J. Shikata, “A Physical-Layer Security Based on Wireless Steganography Through OFDM and DFT-Precoded OFDM Signals,” in Proceedings of IEEE Vehicular Technology Conference (VTC2020-Spring), 2020, pp. 1–5.
  80. R. Tang, D. Gao, M. Yang, T. Guo, H. Wu, and G. Shi, “Gan-inspired intelligent jamming and anti-jamming strategy for semantic communication systems,” in Proceeding of IEEE International Conference on Communications Workshops (ICC WKSHPS), 2023, pp. 1623–1628.
  81. C. Zhao, H. Du, D. Niyato, J. Kang, Z. Xiong, D. I. Kim, K. B. Letaief et al., “Generative AI for Secure Physical Layer Communications: A Survey,” arXiv preprint arXiv:2402.13553, 2024.
  82. Y. Chen, Q. Yang, Z. Shi, and J. Chen, “The model inversion eavesdropping attack in semantic communication systems,” in Proceedings of IEEE Global Communications Conference (GLOBECOM).   IEEE, 2023, pp. 5171–5177.
  83. Q. Qin, Y. Rong, G. Nan, S. Wu, X. Zhang, Q. Cui, and X. Tao, “Securing Semantic Communications with Physical-Layer Semantic Encryption and Obfuscation,” in Proceedings of IEEE International Conference on Communications (ICC), 2023, pp. 5608–5613.
  84. X. Luo, Z. Chen, M. Tao, and F. Yang, “Encrypted Semantic Communication Using Adversarial Training for Privacy Preserving,” IEEE Communications Letters, vol. 27, no. 6, pp. 1486–1490, 2023.
  85. Y. Wang, W. Yang, P. Guan, Y. Zhao, and Z. Xiong, “STAR-RIS-Assisted Privacy Protection in Semantic Communication System,” arXiv preprint arXiv:2306.12675, 2023.
  86. H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, J. Zhang, and X. Shen, “Semantic Communications for Wireless Sensing: RIS-Aided Encoding and Self-Supervised Decoding,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 8, pp. 2547–2562, 2023.
  87. X. Li, Y. Pei, X. Yue, Y. Liu, and Z. Ding, “Secure Communication of Active RIS Assisted NOMA Networks,” IEEE Transactions on Wireless Communications, 2023, doi: 10.1109/TWC.2023.3319450.
  88. L. Hu, S. Tan, H. Wen, J. Wu, J. Fan, S. Chen, and J. Tang, “Interference Alignment for Physical Layer Security in Multi-User Networks With Passive Eavesdroppers,” IEEE Transactions on Information Forensics and Security, vol. 18, pp. 3692–3705, 2023.
  89. J. Lin, Q. Li, J. Yang, H. Shao, and W.-Q. Wang, “Physical-Layer Security for Proximal Legitimate User and Eavesdropper: A Frequency Diverse Array Beamforming Approach,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 3, pp. 671–684, 2018.
  90. Z. Li, J. Li, Y. Liu, X. Liang, K. G. Shin, Z. Yan, and H. Li, “Exploiting Interactions of Multiple Interference for Cooperative Interference Alignment,” IEEE Transactions on Wireless Communications, vol. 20, no. 11, pp. 7072–7085, 2021.
  91. R. Kaewpuang, M. Xu, W. Y. B. Lim, D. Niyato, H. Yu, J. Kang, and X. S. Shen, “Cooperative Resource Management in Quantum Key Distribution (QKD) Networks for Semantic Communication,” IEEE Internet of Things Journal, vol. 11, no. 3, pp. 4454–4469, 2024.
  92. U. Khalid, M. S. Ulum, A. Farooq, T. Q. Duong, O. A. Dobre, and H. Shin, “Quantum Semantic Communications for Metaverse: Principles and Challenges,” IEEE Wireless Communications, vol. 30, no. 4, pp. 26–36, 2023.
  93. M. Min, W. Wang, L. Xiao, Y. Xiao, and Z. Han, “Reinforcement learning-based sensitive semantic location privacy protection for VANETs,” China Communications, vol. 18, no. 6, pp. 244–260, 2021.
  94. L. Zhao, D. Wu, and L. Zhou, “Data Utilization Versus Privacy Protection in Semantic Communications,” IEEE Wireless Communications, vol. 30, no. 3, pp. 44–50, 2023.
  95. B. Knott, S. Venkataraman, A. Hannun, S. Sengupta, M. Ibrahim, and L. van der Maaten, “CrypTen: Secure Multi-Party Computation Meets Machine Learning,” in Proceedings of Advances in Neural Information Processing Systems (NeurIPS), vol. 34, 2021, pp. 4961–4973.
  96. L. T. Phong, Y. Aono, T. Hayashi, L. Wang, and S. Moriai, “Privacy-Preserving Deep Learning via Additively Homomorphic Encryption,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 5, pp. 1333–1345, 2018.
  97. X. Liang, S. Shetty, D. Tosh, C. Kamhoua, K. Kwiat, and L. Njilla, “ProvChain: A Blockchain-Based Data Provenance Architecture in Cloud Environment with Enhanced Privacy and Availability,” in Proceedings of 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2017, pp. 468–477.
  98. Y. Wang, Z. Su, J. Li, N. Zhang, K. Zhang, K.-K. R. Choo, and Y. Liu, “Blockchain-Based Secure and Cooperative Private Charging Pile Sharing Services for Vehicular Networks,” IEEE Transactions on Vehicular Technology, vol. 71, no. 2, pp. 1857–1874, 2022.
  99. N. Fotiou and G. C. Polyzos, “Decentralized name-based security for content distribution using blockchains,” in Proceedings of IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2016, pp. 415–420.
  100. C. Bian, Y. Shao, H. Wu, and D. Gunduz, “Deep Joint Source-Channel Coding Over Cooperative Relay Networks,” arXiv preprint arXiv:2211.06705, 2022.
  101. Y. Lil, X. Zhou, and J. Zhao, “Resource allocation for semantic communication under physical-layer security,” in Proceeding of IEEE Global Communications Conference (GLOBECOM), 2023, pp. 2063–2068.
  102. J. Chen, Z. Zhan, K. He, R. Du, D. Wang, and F. Liu, “XAuth: Efficient Privacy-Preserving Cross-Domain Authentication,” IEEE Transactions on Dependable and Secure Computing, vol. 19, no. 5, pp. 3301–3311, 2022.
  103. Y. Liu, L. Kong, and G. Chen, “Data-oriented mobile crowdsensing: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2849–2885, 2019.
  104. M. Jiang, C. Hou, A. Zheng, S. Han, H. Huang, Q. Wen, X. Hu, and Y. Zhao, “Adgym: Design choices for deep anomaly detection,” vol. 36, 2024.
  105. U. Jayasinghe, G. M. Lee, T.-W. Um, and Q. Shi, “Machine Learning Based Trust Computational Model for IoT Services,” IEEE Transactions on Sustainable Computing, vol. 4, no. 1, pp. 39–52, 2019.
  106. S. Gyawali, Y. Qian, and R. Q. Hu, “Deep Reinforcement Learning Based Dynamic Reputation Policy in 5G Based Vehicular Communication Networks,” IEEE Transactions on Vehicular Technology, vol. 70, no. 6, pp. 6136–6146, 2021.
  107. J. Wang, X. Jing, Z. Yan, Y. Fu, W. Pedrycz, and L. T. Yang, “A Survey on Trust Evaluation Based on Machine Learning,” ACM Computing Surveys, vol. 53, no. 5, pp. 1–36, 2021.
  108. N. B. Truong, G. M. Lee, T.-W. Um, and M. Mackay, “Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 10, pp. 2705–2719, 2019.
  109. Y. Wang, Z. Su, N. Zhang, J. Chen, X. Sun, Z. Ye, and Z. Zhou, “SPDS: A Secure and Auditable Private Data Sharing Scheme for Smart Grid Based on Blockchain,” IEEE Transactions on Industrial Informatics, vol. 17, no. 11, pp. 7688–7699, 2021.
  110. N. Gao, Q. Huang, C. Li, S. Jin, and M. Matthaiou, “EsaNet: Environment Semantics Enabled Physical Layer Authentication,” arXiv preprint arXiv:2307.08946, 2023.
  111. E. Jorswieck, S. Tomasin, and A. Sezgin, “Broadcasting Into the Uncertainty: Authentication and Confidentiality by Physical-Layer Processing,” Proceedings of the IEEE, vol. 103, no. 10, pp. 1702–1724, 2015.
  112. H. Tan, N. Xie, and A. X. Liu, “An Optimization Framework for Active Physical-Layer Authentication,” IEEE Transactions on Mobile Computing, vol. 23, no. 1, pp. 164–179, 2024.
  113. Z. Bao, D. He, M. K. Khan, M. Luo, and Q. Xie, “PBidm: Privacy-Preserving Blockchain-Based Identity Management System for Industrial Internet of Things,” IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1524–1534, 2023.
  114. J. Xu, K. Xue, H. Tian, J. Hong, D. S. L. Wei, and P. Hong, “An Identity Management and Authentication Scheme Based on Redactable Blockchain for Mobile Networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 6, pp. 6688–6698, 2020.
  115. M. Shen, H. Liu, L. Zhu, K. Xu, H. Yu, X. Du, and M. Guizani, “Blockchain-Assisted Secure Device Authentication for Cross-Domain Industrial IoT,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 5, pp. 942–954, 2020.
  116. J. Wang, Z. Yan, H. Wang, T. Li, and W. Pedrycz, “A Survey on Trust Models in Heterogeneous Networks,” IEEE Communications Surveys & Tutorials, vol. 24, no. 4, pp. 2127–2162, 2022.
  117. Q. Wang and D. Wang, “Understanding failures in security proofs of multi-factor authentication for mobile devices,” IEEE Transactions on Information Forensics and Security, vol. 18, pp. 597–612, 2023.
  118. N. H. Sultan, V. Varadharajan, L. Zhou, and F. A. Barbhuiya, “A Role-Based Encryption (RBE) Scheme for Securing Outsourced Cloud Data in a Multi-Organization Context,” IEEE Transactions on Services Computing, vol. 16, no. 3, pp. 1647–1661, 2023.
  119. Y. Xue, K. Xue, N. Gai, J. Hong, D. S. L. Wei, and P. Hong, “An Attribute-Based Controlled Collaborative Access Control Scheme for Public Cloud Storage,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 11, pp. 2927–2942, 2019.
  120. R. Xu, J. Joshi, and P. Krishnamurthy, “An Integrated Privacy Preserving Attribute-Based Access Control Framework Supporting Secure Deduplication,” IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 2, pp. 706–721, 2021.
  121. T. Xue, Y. Wen, B. Luo, G. Li, Y. Li, B. Zhang, Y. Zheng, Y. Hu, and D. Meng, “SparkAC: Fine-Grained Access Control in Spark for Secure Data Sharing and Analytics,” IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 2, pp. 1104–1123, 2023.
  122. H. Wang, Q. Wang, and D. He, “Blockchain-Based Private Provable Data Possession,” IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 5, pp. 2379–2389, 2021.
  123. G. Ateniese, B. Magri, D. Venturi, and E. Andrade, “Redactable Blockchain – or – Rewriting History in Bitcoin and Friends,” in Proceedings of IEEE European Symposium on Security and Privacy (EuroS&P), 2017, pp. 111–126.
  124. A. Bahramali, M. Nasr, A. Houmansadr, D. Goeckel, and D. Towsley, “Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems,” in Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS), 2021, pp. 126–140.
  125. R. Xia, D. Liu, J. Li, L. Yuan, N. Wang, and X. Gao, “Mmnet: Multi-collaboration and multi-supervision network for sequential deepfake detection,” IEEE Transactions on Information Forensics and Security, vol. 19, pp. 3409–3422, 2024.
  126. H. Oz, A. Aris, A. Levi, and A. S. Uluagac, “A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions,” ACM Computing Surveys, vol. 54, no. 11s, pp. 1–37, 2022.
  127. T. McIntosh, A. Kayes, Y.-P. P. Chen, A. Ng, and P. Watters, “Ransomware mitigation in the modern era: A comprehensive review, research challenges, and future directions,” ACM Computing Surveys (CSUR), vol. 54, no. 9, pp. 1–36, 2021.
  128. Z. Yang, M. Chen, G. Li, Y. Yang, and Z. Zhang, “Secure Semantic Communications: Fundamentals and Challenges,” arXiv preprint arXiv:2301.01421, 2023.
  129. H. Xie, Z. Qin, X. Tao, and Z. Han, “Towards intelligent communications: Large model empowered semantic communications,” arXiv preprint arXiv:2402.13073, 2024.
  130. L. Zhu, Z. Liu, and S. Han, “Deep leakage from gradients,” in Proceeding of Advances in neural information processing systems (NIPS), pp. 14 747–14 756.
  131. W. Yang, X. Chi, L. Zhao, Z. Xiong, and W. Jiang, “Task-driven Semantic-aware Green Cooperative Transmission Strategy for Vehicular Networks,” IEEE Transactions on Communications, pp. 5783–5798, 2023.
  132. M. G. Gaber, M. Ahmed, and H. Janicke, “Malware detection with artificial intelligence: A systematic literature review,” ACM Computing Surveys, vol. 56, no. 6, pp. 1–33, 2024.
  133. Q. Zhou, R. Li, Z. Zhao, Y. Xiao, and H. Zhang, “Adaptive Bit Rate Control in Semantic Communication With Incremental Knowledge-Based HARQ,” IEEE Open Journal of the Communications Society, vol. 3, pp. 1076–1089, 2022.
  134. F. Wang, Y. Hong, and X. Ban, “Infrastructure-enabled gps spoofing detection and correction,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 9, pp. 9462–9475, 2023.
  135. R. Zhao, Q. Qin, N. Xu, G. Nan, Q. Cui, and X. Tao, “SemKey: Boosting Secret Key Generation for RIS-assisted Semantic Communication Systems,” in Proceedings of IEEE Vehicular Technology Conference (VTC2022-Fall), 2022, pp. 1–5.
  136. A. D. Wyner, “The wire-tap channel,” The Bell System Technical Journal, vol. 54, no. 8, pp. 1355–1387, 1975.
  137. Y. Liu, H.-H. Chen, and L. Wang, “Physical Layer Security for Next Generation Wireless Networks: Theories, Technologies, and Challenges,” IEEE Communications Surveys & Tutorials, vol. 19, no. 1, pp. 347–376, 2017.
  138. S. Goel and R. Negi, “Guaranteeing Secrecy using Artificial Noise,” IEEE Transactions on Wireless Communications, vol. 7, no. 6, pp. 2180–2189, 2008.
  139. X. Chen, J. An, Z. Xiong, C. Xing, N. Zhao, F. R. Yu, and A. Nallanathan, “Covert Communications: A Comprehensive Survey,” IEEE Communications Surveys & Tutorials, vol. 25, no. 2, pp. 1173–1198, 2023.
  140. M. Chehimi, C. Chaccour, C. K. Thomas, and W. Saad, “Quantum semantic communications for resource-efficient quantum networking,” IEEE Communications Letters, 2024, doi:10.1109/LC0MM.2024.3361852.
  141. E. Erdemir, T.-Y. Tung, P. L. Dragotti, and D. Gündüz, “Generative Joint Source-Channel Coding for Semantic Image Transmission,” IEEE Journal on Selected Areas in Communications, pp. 1–1, 2023.
  142. H. Du, G. Liu, D. Niyato, J. Zhang, J. Kang, Z. Xiong, B. Ai, and D. I. Kim, “Generative AI-aided Joint Training-free Secure Semantic Communications via Multi-modal Prompts,” arXiv preprint arXiv:2309.02616, 2023.
  143. J. Chen, D. You, D. Gündüz, and P. L. Dragotti, “CommIN: Semantic Image Communications as an Inverse Problem with INN-Guided Diffusion Models,” arXiv preprint arXiv:2310.01130, 2023.
  144. T. Han, J. Tang, Q. Yang, Y. Duan, Z. Zhang, and Z. Shi, “Generative Model based Highly Efficient Semantic Communication Approach for Image Transmission,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, pp. 1–5.
  145. S. Tonmoy, S. Zaman, V. Jain, A. Rani, V. Rawte, A. Chadha, and A. Das, “A comprehensive survey of hallucination mitigation techniques in large language models,” arXiv preprint arXiv:2401.01313, 2024.
  146. S. Kim, K. Shim, L. T. Nguyen, and B. Shim, “Semantic-Preserving Augmentation for Robust Image-Text Retrieval,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, pp. 1–5.
  147. F. Zhao, Y. Sun, L. Feng, L. Zhang, and D. Zhao, “Enhancing reasoning ability in semantic communication through generative ai-assisted knowledge construction,” IEEE Communications Letters, 2024, doi: 10.1109/LCOMM.2024.3365158.
  148. S. Wang, J. Dai, Z. Liang, K. Niu, Z. Si, C. Dong, X. Qin, and P. Zhang, “Wireless Deep Video Semantic Transmission,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 1, pp. 214–229, 2023.
  149. W. Gong, H. Tong, S. Wang, Z. Yang, X. He, and C. Yin, “Adaptive Bitrate Video Semantic Communication over Wireless Networks,” arXiv preprint arXiv:2308.00531, 2023.
  150. S. Jiang, Y. Liu, Y. Zhang, P. Luo, K. Cao, J. Xiong, H. Zhao, and J. Wei, “Reliable Semantic Communication System Enabled by Knowledge Graph,” Entropy, vol. 24, no. 6, p. 846, 2022.
  151. P. Yao and D. Barbosa, “Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out,” in Proceedings of the Web Conference (WWW), 2021, pp. 3305–3313.
  152. C. K. Thomas and W. Saad, “Neuro-Symbolic Causal Reasoning Meets Signaling Game for Emergent Semantic Communications,” IEEE Transactions on Wireless Communications, 2023, doi: 10.1109/TWC.2023.3319981.
  153. W. Wang, Y. Yang, and F. Wu, “Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing,” arXiv preprint arXiv:2210.15889, 2022.
  154. N. Xie, Z. Li, and H. Tan, “A Survey of Physical-Layer Authentication in Wireless Communications,” IEEE Communications Surveys & Tutorials, vol. 23, no. 1, pp. 282–310, 2021.
  155. J. B. Perazzone, P. L. Yu, B. M. Sadler, and R. S. Blum, “Artificial Noise-Aided MIMO Physical Layer Authentication With Imperfect CSI,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 2173–2185, 2021.
  156. J. Xie, H. Tang, T. Huang, F. R. Yu, R. Xie, J. Liu, and Y. Liu, “A Survey of Blockchain Technology Applied to Smart Cities: Research Issues and Challenges,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2794–2830, 2019.
  157. E. Parhizkar, M. H. Nikravan, R. C. Holte, and S. Zilles, “Combining direct trust and indirect trust in multi-agent systems.” in Proceeding of International Joint Conference on Artificial Intelligence (IJCAI), 2020, pp. 311–317.
  158. A. Sekhari, J. Acharya, G. Kamath, and A. T. Suresh, “Remember What You Want to Forget: Algorithms for Machine Unlearning,” in Proceedings of Advances in Neural Information Processing Systems (NeurIPS), vol. 34, 2021, pp. 18 075–18 086.
  159. H. Wei, W. Ni, W. Xu, F. Wang, D. Niyato, and P. Zhang, “Federated Semantic Learning Driven by Information Bottleneck for Task-Oriented Communications,” IEEE Communications Letters, vol. 27, no. 10, pp. 2652–2656, 2023.
  160. H. Xu, T. Zhu, L. Zhang, W. Zhou, and P. S. Yu, “Machine Unlearning: A Survey,” ACM Computing Surveys, vol. 56, no. 1, pp. 1–36, 2024.
  161. A. Golatkar, A. Achille, and S. Soatto, “Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 9301–9309.
  162. M. Chen, Z. Zhang, T. Wang, M. Backes, M. Humbert, and Y. Zhang, “When Machine Unlearning Jeopardizes Privacy,” in Proceedings of ACM SIGSAC Conference on Computer and Communications Security (CCS), 2021, pp. 896–911.
  163. J. Chen, J. Wang, C. Jiang, Y. Ren, and L. Hanzo, “Trustworthy Semantic Communications for the Metaverse Relying on Federated Learning,” IEEE Wireless Communications, vol. 30, no. 4, pp. 18–25, 2023.
  164. P. Jauernig, A.-R. Sadeghi, and E. Stapf, “Trusted Execution Environments: Properties, Applications, and Challenges,” IEEE Security & Privacy, vol. 18, no. 2, pp. 56–60, 2020.
  165. H. Du, J. Liu, D. Niyato, J. Kang, Z. Xiong, J. Zhang, and D. I. Kim, “Attention-Aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services,” IEEE Journal on Selected Areas in Communications, vol. 41, no. 7, pp. 2158–2175, 2023.
  166. Y. Xiao, G. Shi, Y. Li, W. Saad, and H. V. Poor, “Toward Self-Learning Edge Intelligence in 6G,” IEEE Communications Magazine, vol. 58, no. 12, pp. 34–40, 2020.
  167. S. Hui, H. Wang, Z. Wang, X. Yang, Z. Liu, D. Jin, and Y. Li, “Knowledge Enhanced GAN for IoT Traffic Generation,” in ProvChain: A Blockchain-Based Data Provenance Architecture in Cloud Environment with Enhanced Privacy and Availab, 2022, pp. 3336–3346.
  168. L. Deng, G. Li, S. Han, L. Shi, and Y. Xie, “Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey,” Proceedings of the IEEE, vol. 108, no. 4, pp. 485–532, 2020.
  169. M. Davies, A. Wild, G. Orchard, Y. Sandamirskaya, G. A. F. Guerra, P. Joshi, P. Plank, and S. R. Risbud, “Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook,” Proceedings of the IEEE, vol. 109, no. 5, pp. 911–934, 2021.
  170. W. Luo, L. Cao, Y. Shi, L. Wan, H. Zhang, S. Li, G. Chen, Y. Li, S. Li, Y. Wang, S. Sun, M. F. Karim, H. Cai, L. C. Kwek, and A. Q. Liu, “Recent progress in quantum photonic chips for quantum communication and internet,” Light: Science & Applications, vol. 12, no. 1, p. 175, 2023.
  171. A. Barredo Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garcia, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, and F. Herrera, “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, vol. 58, pp. 82–115, 2020.
  172. A. Rawal, J. McCoy, D. B. Rawat, B. M. Sadler, and R. S. Amant, “Recent Advances in Trustworthy Explainable Artificial Intelligence: Status, Challenges, and Perspectives,” IEEE Transactions on Artificial Intelligence, vol. 3, no. 6, pp. 852–866, 2022.
  173. J. Wu, “Development paradigms of cyberspace endogenous safety and security,” Science China Information Sciences, vol. 65, no. 5, p. 156301, 2022.
Citations (5)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now