  Semantic communication (SemCom) enhances transmission efficiency by sending
only task-relevant information compared to traditional methods. However,
transmitting semantic-rich data over insecure or public channels poses security
and privacy risks. This paper addresses the privacy problem of transmitting
images over wiretap channels and proposes a novel SemCom approach ensuring
privacy through a differential privacy (DP)-based image protection and
deprotection mechanism. The method utilizes the GAN inversion technique to
extract disentangled semantic features and applies a DP mechanism to protect
sensitive features within the extracted semantic information. To address the
non-invertibility of DP, we introduce two neural networks to approximate the DP
application and removal processes, offering a privacy protection level close to
that by the original DP process. Simulation results validate the effectiveness
of our method in preventing eavesdroppers from obtaining sensitive information
while maintaining high-fidelity image reconstruction at the legitimate
receiver.
