Disentangled Information Bottleneck guided Privacy-Protective JSCC for Image Transmission

TL;DR

This paper introduces a novel privacy-protective JSCC method for image transmission that disentangles private and public information, encrypts private data, and maintains high transmission quality, significantly reducing privacy leakage and inference time.

Contribution

It proposes a disentangled information bottleneck approach combined with a password-based encryption scheme for privacy protection in JSCC, with a differentiable DIB objective for neural network optimization.

Findings

Reduces eavesdropping accuracy on private info by up to 15%

Decreases inference time by 10% compared to existing methods

Achieves superior communication performance while protecting privacy

Abstract

Joint source and channel coding (JSCC) has attracted increasing attention due to its robustness and high efficiency. However, JSCC is vulnerable to privacy leakage due to the high relevance between the source image and channel input. In this paper, we propose a disentangled information bottleneck guided privacy-protective JSCC (DIB-PPJSCC) for image transmission, which aims at protecting private information as well as achieving superior communication performance at the legitimate receiver. In particular, we propose a DIB objective to disentangle private and public information. The goal is to compress the private information in the public subcodewords, preserve the private information in the private subcodewords and improve the reconstruction quality simultaneously. In order to optimize JSCC neural networks using the DIB objective, we derive a differentiable estimation of the DIB objective based on the variational approximation and the density-ratio trick. Additionally, we design a password-based privacy-protective (PP) algorithm which can be jointly optimized with JSCC neural networks to encrypt the private subcodewords. Specifically, we employ a private information encryptor to encrypt the private subcodewords before transmission, and a corresponding decryptor to recover the private information at the legitimate receiver. A loss function for jointly training the encryptor, decryptor and JSCC decoder is derived based on the maximum entropy principle, which aims at maximizing the eavesdropping uncertainty as well as improving the reconstruction quality. Experimental results show that DIB-PPJSCC can reduce the eavesdropping accuracy on private information up to $15\%$ and reduce $10\%$ inference time compared to existing privacy-protective JSCC and traditional separate methods.