DRAG: Data Reconstruction Attack using Guided Diffusion
Journal
Proceedings of Machine Learning Research
Journal Volume
267
Start Page
33883
End Page
33901
ISSN
26403498
Date Issued
2025-07
Author(s)
Abstract
With the rise of large foundation models, split inference (SI) has emerged as a popular computational paradigm for deploying models across lightweight edge devices and cloud servers, addressing data privacy and computational cost concerns. However, most existing data reconstruction attacks have focused on smaller CNN classification models, leaving the privacy risks of foundation models in SI settings largely unexplored. To address this gap, we propose a novel data reconstruction attack based on guided diffusion, which leverages the rich prior knowledge embedded in a latent diffusion model (LDM) pre-trained on a large-scale dataset. Our method performs iterative reconstruction on the LDM’s learned image prior, effectively generating high-fidelity images resembling the original data from their intermediate representations (IR). Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods, both qualitatively and quantitatively, in reconstructing data from deep-layer IRs of the vision foundation model. The results highlight the urgent need for more robust privacy protection mechanisms for large models in SI scenarios. Code is available at: https: //github.com/ntuaislab/DRAG
Event(s)
42nd International Conference on Machine Learning, ICML 2025
Publisher
ML Research Press
Type
conference paper