Disentangled Information Bottleneck Guided Privacy-Protective Joint Source and Channel Coding for Image Transmission

Joint source and channel coding (JSCC) has attracted increasing attention in semantic communications. However, JSCC is vulnerable to privacy issues due to the high relevance between the source image and channel input. In this paper, we propose a disentangled information bottleneck guided privacy-pro...

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Bibliographic Details
Published in:IEEE transactions on communications Vol. 72; no. 11; pp. 6992 - 7005
Main Authors: Sun, Lunan, Yang, Yang, Chen, Mingzhe, Guo, Caili
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Coding
Design optimization
Distortion
Eavesdropping
Encryption
Image communication
Image quality
Image reconstruction
Image transmission
information bottleneck
Joint source and channel coding
Maximum entropy
Neural networks
Noise measurement
Privacy
privacy protection
Protection
semantic communication
ISSN:0090-6778, 1558-0857
Online Access:Get full text
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Summary:Joint source and channel coding (JSCC) has attracted increasing attention in semantic communications. However, JSCC is vulnerable to privacy issues 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 (DPJSCC) for image transmission, which aims at protecting private information and achieving superior image transmission performance. In particular, we propose a disentangled information bottleneck objective to compress the private information in public subcodewords and improve the reconstruction quality simultaneously. To optimize JSCC neural networks using the proposed objective, we derive a differentiable estimation based on variational approximation and the density-ratio trick. Additionally, we design a password-based privacy-protective algorithm that encrypts the private subcodewords, achieving joint optimization with JSCC neural networks. The proposed algorithm involves an encryptor for encrypting private information and a decryptor for recovering it at the legitimate receiver. A loss function is derived based on the maximum entropy principle for jointly training the encryptor, decryptor, and JSCC decoder to maximize eavesdropping uncertainty and improve reconstruction quality. Experimental results show that DPJSCC reduces eavesdropping accuracy on private information by up to 18% and decreases inference time by 10%.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2024.3406381