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

• URL: http://arxiv.org/abs/2309.08188v1
• Date: Fri, 15 Sep 2023 06:34:22 GMT
• Title: Privacy-Aware Joint Source-Channel Coding for image transmission based on Disentangled Information Bottleneck
• Authors: Lunan Sun, Caili Guo, Mingzhe Chen, Yang Yang,
• Abstract summary: Current privacy-aware joint source-channel coding (JSCC) works aim at avoiding private information transmission by adversarially training the J SCC encoder and decoder. We propose a novel privacy-aware J SCC based on disentangled information bottleneck (DIB-PAJSCC) We show that DIB-PAJSCC can reduce the eavesdropping accuracy on private information by up to 20% compared to existing methods.
• Score: 27.929075969353764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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