SecMdp: Towards Privacy-Preserving Multimodal Deep Learning in End-Edge-Cloud

Multimodal deep learning technologies have advanced significantly, which brings extensive applications in diverse fields. The substantial computational demands of training and prediction in multimodal deep learning have made the End-Edge-Cloud (EEC) framework popular. It is essential to protect multimodal data and model privacy in such a framework. However, traditional cryptographic methods, though secure for data and models at edge nodes, cause efficiency limitations. In this paper, we propose SecMdp, an SGX-assisted secure computational framework for multimodal data in the EEC architecture. Edge nodes are equipped with the trusted execution environment (e.g., Intel SGX) to run multimodal algorithms. Additionally, to address the side-channel attacks of SGX, we present an enhanced PathORAM algorithm, MM_PathORAM, for the multimodal training and prediction processes, which are tailored for multimodal deep learning scenarios. It accelerates multimodal data access while protecting data privacy and model security. Experimental evaluation supports the effectiveness of our design in preserving edge computing efficiency. It demonstrates negligible impact on the speed of multimodal data loading, the configuration of model parameters during training, or the accuracy of predictions.