Privacy-Preserving Task-Oriented Semantic Communications Against Model Inversion Attacks

Semantic communication has been identified as a core technology for the sixth generation (6G) of wireless networks. Recently, task-oriented semantic communications have been proposed for low-latency inference with limited bandwidth. Although transmitting only task-related information does protect a...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2024-08, Vol.23 (8), p.10150-10165
Main Authors: Wang, Yanhu, Guo, Shuaishuai, Deng, Yiqin, Zhang, Haixia, Fang, Yuguang
Format: Article
Language:English
Subjects:
adversarial learning
Communication systems
Feature extraction
Image reconstruction
information bottleneck
Privacy
privacy-preservation
Semantic communications
Semantics
Task analysis
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Summary:Semantic communication has been identified as a core technology for the sixth generation (6G) of wireless networks. Recently, task-oriented semantic communications have been proposed for low-latency inference with limited bandwidth. Although transmitting only task-related information does protect a certain level of user privacy, adversaries could apply model inversion techniques to reconstruct the raw data or extract useful information, thereby infringing on users' privacy. To mitigate privacy infringement, this paper proposes an information bottleneck and adversarial learning (IBAL) approach to protect users' privacy against model inversion attacks. Specifically, we extract task-relevant features from the input based on the information bottleneck (IB) theory. To overcome the difficulty in calculating the mutual information in high-dimensional space, we derive a variational upper bound to estimate the true mutual information. To prevent data reconstruction from task-related features by adversaries, we leverage adversarial learning to train encoder to fool adversaries by maximizing reconstruction distortion. Furthermore, considering the impact of channel variations on privacy-utility trade-off and the difficulty in manually tuning the weights of each loss, we propose an adaptive weight adjustment method. Numerical results demonstrate that the proposed approaches can effectively protect privacy without significantly affecting task performance and achieve better privacy-utility trade-offs than baseline methods.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2024.3369170