Contrastive Learning and Adversarial Disentanglement for Privacy-Preserving Task-Oriented Semantic Communications
Task-oriented semantic communication systems have emerged as a promising approach to achieving efficient and intelligent data transmission, where only information relevant to a specific task is communicated. However, existing methods struggle to fully disentangle task-relevant and task-irrelevant in...
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Zusammenfassung: | Task-oriented semantic communication systems have emerged as a promising
approach to achieving efficient and intelligent data transmission, where only
information relevant to a specific task is communicated. However, existing
methods struggle to fully disentangle task-relevant and task-irrelevant
information, leading to privacy concerns and subpar performance. To address
this, we propose an information-bottleneck method, named CLAD (contrastive
learning and adversarial disentanglement). CLAD leverages contrastive learning
to effectively capture task-relevant features while employing adversarial
disentanglement to discard task-irrelevant information. Additionally, due to
the lack of reliable and reproducible methods to gain insight into the
informativeness and minimality of the encoded feature vectors, we introduce a
new technique to compute the information retention index (IRI), a comparative
metric used as a proxy for the mutual information between the encoded features
and the input, reflecting the minimality of the encoded features. The IRI
quantifies the minimality and informativeness of the encoded feature vectors
across different task-oriented communication techniques. Our extensive
experiments demonstrate that CLAD outperforms state-of-the-art baselines in
terms of task performance, privacy preservation, and IRI. CLAD achieves a
predictive performance improvement of around 2.5-3%, along with a 77-90%
reduction in IRI and a 57-76% decrease in adversarial accuracy. |
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DOI: | 10.48550/arxiv.2410.22784 |