DPG-DT: Differentially Private Generative Digital Twin for Imbalanced Learning in Industrial IoT

The existing Artificial Intelligence (AI)-based industrial defect detection methods have received extensive attention in the industrial Internet of Things (IoT). However, due to the limited defect samples, it is difficult for discriminative models to achieve better performance in imbalanced learning. In addition, the privacy concerns surrounding sensitive information hinder the sharing of synthetic industrial data. In this paper, we propose a novel framework called the Differentially Private Generative AI-empowered Digital Twin (DPG-DT) framework, aiming to synthesize realistic samples while satisfying differential privacy and empowering the construction of digital space and its connection with physical space. Specifically, the core of the DPG-DT framework is the proposed Private Synthetic Industry Energy-guided model (PSIE), in which we privatize the energybased model-empowered Langevin Markov Chain Monte Carlo (MCMC) sampling method with Gaussian noise and random response. Our method could replace the conventional generator while guaranteeing privacy. Extensive experiments on real-world industrial datasets NEU-CLS and DeepPCB demonstrate that the proposed framework is capable of generating synthetic industrial images with both high fidelity and differential privacy. Moreover, the achieved downstream accuracy outperforms baselines by 23.9 % in industrial scenarios.