  Trustworthy task-oriented semantic communication (ToSC) emerges as an
innovative approach in the 6G landscape, characterized by the transmission of
only vital information that is directly pertinent to a specific task. While
ToSC offers an efficient mode of communication, it concurrently raises concerns
regarding privacy, as sophisticated adversaries might possess the capability to
reconstruct the original data from the transmitted features. This article
provides an in-depth analysis of privacy-preserving strategies specifically
designed for ToSC relying on deep neural network-based joint source and channel
coding (DeepJSCC). The study encompasses a detailed comparative assessment of
trustworthy feature perturbation methods such as differential privacy and
encryption, alongside intrinsic security incorporation approaches like
adversarial learning to train the JSCC and learning-based vector quantization
(LBVQ). This comparative analysis underscores the integration of advanced
explainable learning algorithms into communication systems, positing a new
benchmark for privacy standards in the forthcoming 6G era.
