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.