Privacy-preserving data release leveraging optimal transport and particle gradient descent

TL;DR

This paper introduces PrivPGD, a novel differentially private data synthesis method that leverages optimal transport and particle gradient descent, outperforming existing marginal-based approaches in scalability and flexibility.

Contribution

The paper presents PrivPGD, a new private data synthesis method that improves performance and scalability over existing marginal-based techniques using optimal transport and particle gradient descent.

Findings

Outperforms existing methods on various datasets

Highly scalable and flexible to domain-specific constraints

Effective in sensitive domains like healthcare and government

Abstract

We present a novel approach for differentially private data synthesis of protected tabular datasets, a relevant task in highly sensitive domains such as healthcare and government. Current state-of-the-art methods predominantly use marginal-based approaches, where a dataset is generated from private estimates of the marginals. In this paper, we introduce PrivPGD, a new generation method for marginal-based private data synthesis, leveraging tools from optimal transport and particle gradient descent. Our algorithm outperforms existing methods on a large range of datasets while being highly scalable and offering the flexibility to incorporate additional domain-specific constraints.