Enhancing Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908351406800896 |
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| author | Chen, Weixuan Tang, Shunpu Yang, Qianqian Shi, Zhiguo Niyato, Dusit |
| author_facet | Chen, Weixuan Tang, Shunpu Yang, Qianqian Shi, Zhiguo Niyato, Dusit |
| contents | Semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information. However, transmitting semantic-rich data over insecure channels introduces privacy risks. This paper proposes a novel SemCom framework that integrates differential privacy (DP) mechanisms to protect sensitive semantic features. This method employs the generative adversarial network (GAN) inversion technique to extract disentangled semantic features and uses neural networks (NNs) to approximate the DP application and removal processes, effectively mitigating the non-invertibility issue of DP. Additionally, an NN-based encryption scheme is introduced to strengthen the security of channel inputs. Simulation results demonstrate that the proposed approach effectively prevents eavesdroppers from reconstructing sensitive information by generating chaotic or fake images, while ensuring high-quality image reconstruction for legitimate users. The system exhibits robust performance across various privacy budgets and channel conditions, achieving an optimal balance between privacy protection and reconstruction fidelity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18581 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy Chen, Weixuan Tang, Shunpu Yang, Qianqian Shi, Zhiguo Niyato, Dusit Cryptography and Security Image and Video Processing Semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information. However, transmitting semantic-rich data over insecure channels introduces privacy risks. This paper proposes a novel SemCom framework that integrates differential privacy (DP) mechanisms to protect sensitive semantic features. This method employs the generative adversarial network (GAN) inversion technique to extract disentangled semantic features and uses neural networks (NNs) to approximate the DP application and removal processes, effectively mitigating the non-invertibility issue of DP. Additionally, an NN-based encryption scheme is introduced to strengthen the security of channel inputs. Simulation results demonstrate that the proposed approach effectively prevents eavesdroppers from reconstructing sensitive information by generating chaotic or fake images, while ensuring high-quality image reconstruction for legitimate users. The system exhibits robust performance across various privacy budgets and channel conditions, achieving an optimal balance between privacy protection and reconstruction fidelity. |
| title | Enhancing Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy |
| topic | Cryptography and Security Image and Video Processing |
| url | https://arxiv.org/abs/2504.18581 |