Privacy-Preserving Data Synthesis for General Purposes

The recent success of deep neural networks (DNNs) hinges on the availability of large-scale datasets; however, training on such datasets often poses privacy risks for sensitive training information, such as face images and medical records of individuals. In this talk, I will mainly discuss how to explore the power of generative models and gradient sparsity, and talk about different scalable privacy-preserving generative models in both centralized and decentralized settings. In particular, I will introduce our recent work on large-scale privacy-preserving data generative models leveraging gradient compression with convergence guarantees. I will also introduce how to train generative models with privacy guarantees in heterogeneous environments, where data of local agents come from diverse distributions. We will finally discuss some potential applications for different privacy-preserving data synthesis strategies.