Open Access

ARTICLE

Entropy-Bottleneck-Based Privacy Protection Mechanism for Semantic Communication

Kaiyang Han¹, Xiaoqiang Jia¹, Yangfei Lin², Tsutomu Yoshinaga², Yalong Li², Jiale Wu^2,*

1 School of Information Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China
2 Department of Computer and Network Engineering, University of Electro-Communications, Chofu, Tokyo, 1828585, Japan

* Corresponding Author: Jiale Wu. Email: .jp

(This article belongs to the Special Issue: Privacy-Preserving Deep Learning and its Advanced Applications)

Computers, Materials & Continua 2025, 83(2), 2971-2988. https://doi.org/10.32604/cmc.2025.061563

Received 27 November 2024; Accepted 13 February 2025; Issue published 16 April 2025

Abstract

With the rapid development of artificial intelligence and the Internet of Things, along with the growing demand for privacy-preserving transmission, the need for efficient and secure communication systems has become increasingly urgent. Traditional communication methods transmit data at the bit level without considering its semantic significance, leading to redundant transmission overhead and reduced efficiency. Semantic communication addresses this issue by extracting and transmitting only the most meaningful semantic information, thereby improving bandwidth efficiency. However, despite reducing the volume of data, it remains vulnerable to privacy risks, as semantic features may still expose sensitive information. To address this, we propose an entropy-bottleneck-based privacy protection mechanism for semantic communication. Our approach uses semantic segmentation to partition images into regions of interest (ROI) and regions of non-interest (RONI) based on the receiver’s needs, enabling differentiated semantic transmission. By focusing transmission on ROIs, bandwidth usage is optimized, and non-essential data is minimized. The entropy bottleneck model probabilistically encodes the semantic information into a compact bit stream, reducing correlation between the transmitted content and the original data, thus enhancing privacy protection. The proposed framework is systematically evaluated in terms of compression efficiency, semantic fidelity, and privacy preservation. Through comparative experiments with traditional and state-of-the-art methods, we demonstrate that the approach significantly reduces data transmission, maintains the quality of semantically important regions, and ensures robust privacy protection.

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Keywords

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