A Unified View of Differentially Private Deep Generative Modeling
Dingfan Chen Raouf Kerkouche Mario Fritz
The availability of rich and vast data sources has greatly advanced machine
learning applications in various domains. However, data with privacy concerns
comes with stringent regulations that frequently prohibited data access and
data sharing. Overcoming these obstacles in compliance with privacy
considerations is key for technological progress in many real-world application
scenarios that involve privacy sensitive data. Differentially private (DP) data
publishing provides a compelling solution, where only a sanitized form of the
data is publicly released, enabling privacy-preserving downstream analysis and
reproducible research in sensitive domains. In recent years, various approaches
have been proposed for achieving privacy-preserving high-dimensional data
generation by private training on top of deep neural networks. In this paper,
we present a novel unified view that systematizes these approaches. Our view
provides a joint design space for systematically deriving methods that cater to
different use cases. We then discuss the strengths, limitations, and inherent
correlations between different approaches, aiming to shed light on crucial
aspects and inspire future research. We conclude by presenting potential paths
forward for the field of DP data generation, with the aim of steering the
community toward making the next important steps in advancing
privacy-preserving learning.