Diffusion-Based Semantic Encoders for Secure and Efficient Edge Computing
Abstract
Mobile Edge Computing (MEC) enables resource-constrained Internet of Things (IoT) devices to offload computation-intensive workloads to nearby edge servers, reducing latency and energy consumption. However, wireless offloading exposes transmitted data to eavesdropping, raising serious privacy concerns. Existing physical-layer security techniques either introduce additional overhead or fail to conceal semantic information. This paper proposes a diffusion-based semantic encoding framework for secure and efficient computation offloading in MEC systems. By applying a forward diffusion process, task inputs are transformed into approximately noise-like latent representations whose distribution is statistically close to a standard Gaussian distribution prior to transmission. Authorized edge servers equipped with learned reverse diffusion models can reliably recover task-relevant semantics, while intercepted representations reveal negligible information. We analyze the secrecy and robustness of diffusion-based encoding from an information-theoretic perspective and integrate the proposed encoder-decoder into a practical MEC offloading pipeline. Extensive experiments under realistic wireless conditions demonstrate that diffusion-based encoding significantly improves secrecy, reduces transmission overhead, and maintains high task performance. Compared with autoencoder and variational autoencoder baselines, the proposed approach offers substantially stronger resistance to reconstruction and inference attacks while preserving computational efficiency at the edge.