Privacy-Aware Joint Source-Channel Coding for image transmission based on Disentangled Information Bottleneck

TL;DR

This paper introduces a privacy-aware joint source-channel coding method for image transmission that uses disentangled information bottleneck to separate private and public data, reducing privacy risks without needing multiple models for different eavesdropper conditions.

Contribution

It proposes a novel disentangled information bottleneck objective for privacy-aware JSCC, enabling transmission of only public information and eliminating the need for multiple models for various eavesdropper SNRs.

Findings

Reduces eavesdropping accuracy on private info by up to 20%.

Eliminates additional training for different eavesdropper SNRs.

Maintains image reconstruction quality with privacy preservation.

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.