Abstract
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 bottleneck objective to disentangle private and
public information. Given the separate information, the transmitter can
transmit only public information to the receiver while minimizing
reconstruction distortion. Since DIB-PAJSCC transmits only public information
regardless of the eavesdroppers' SNRs, it can eliminate additional training
adapted to eavesdroppers' SNRs. Experimental results show that DIB-PAJSCC can
reduce the eavesdropping accuracy on private information by up to 20\% compared
to existing methods.