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.

翻译：联合信源信道编码（JSCC）因其鲁棒性和高效性而日益受到关注。然而，由于源图像与信道输入之间存在高度相关性，JSCC容易遭受隐私泄露。本文提出一种解耦信息瓶颈驱动的隐私保护联合信源信道编码（DIB-PPJSCC）用于图像传输，旨在保护私有信息的同时，在合法接收端实现优越的通信性能。具体而言，我们提出一个解耦信息瓶颈（DIB）目标以分离私有信息与公共信息。其目标是在公共子码字中压缩私有信息，在私有子码字中保留私有信息，并同时提升重建质量。为利用DIB目标优化JSCC神经网络，我们基于变分近似和密度比技巧推导了DIB目标的可微估计量。此外，我们设计了一种基于密码的隐私保护（PP）算法，该算法可与JSCC神经网络联合优化以加密私有子码字。具体地，我们在传输前使用私有信息加密器对私有子码字进行加密，并在合法接收端使用相应的解密器恢复私有信息。基于最大熵原理，我们推导了用于联合训练加密器、解密器和JSCC解码器的损失函数，旨在最大化窃听不确定性并提升重建质量。实验结果表明，与现有隐私保护JSCC及传统分离方法相比，DIB-PPJSCC可将私有信息的窃听准确率降低高达15%，并减少10%的推理时间。