  Semantic communication has been identified as a core technology for the sixth
generation (6G) of wireless networks. Recently, task-oriented semantic
communications have been proposed for low-latency inference with limited
bandwidth. Although transmitting only task-related information does protect a
certain level of user privacy, adversaries could apply model inversion
techniques to reconstruct the raw data or extract useful information, thereby
infringing on users' privacy. To mitigate privacy infringement, this paper
proposes an information bottleneck and adversarial learning (IBAL) approach to
protect users' privacy against model inversion attacks. Specifically, we
extract task-relevant features from the input based on the information
bottleneck (IB) theory. To overcome the difficulty in calculating the mutual
information in high-dimensional space, we derive a variational upper bound to
estimate the true mutual information. To prevent data reconstruction from
task-related features by adversaries, we leverage adversarial learning to train
encoder to fool adversaries by maximizing reconstruction distortion.
Furthermore, considering the impact of channel variations on privacy-utility
trade-off and the difficulty in manually tuning the weights of each loss, we
propose an adaptive weight adjustment method. Numerical results demonstrate
that the proposed approaches can effectively protect privacy without
significantly affecting task performance and achieve better privacy-utility
trade-offs than baseline methods.
