Sept. 18, 2023, 1:10 a.m. | Lunan Sun, Caili Guo, Mingzhe Chen, Yang Yang

cs.CR updates on

Current privacy-aware joint source-channel coding (JSCC) works aim at
avoiding private information transmission by adversarially training the JSCC
encoder and decoder under specific signal-to-noise ratios (SNRs) of
eavesdroppers. However, these approaches incur additional computational and
storage requirements as multiple neural networks must be trained for various
eavesdroppers' SNRs to determine the transmitted information. To overcome this
challenge, we propose a novel privacy-aware JSCC for image transmission based
on disentangled information bottleneck (DIB-PAJSCC). In particular, we derive a
novel disentangled information …

aim aware channel coding computational current decoder image information networks neural networks noise privacy private requirements signal storage training transmission under

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