HeteroFedSyn: Differentially Private Tabular Data Synthesis for Heterogeneous Federated Settings

TL;DR

HeteroFedSyn is a novel differentially private data synthesis framework tailored for heterogeneous federated settings, enabling high-utility synthetic tabular data sharing without compromising privacy.

Contribution

It introduces a federated DP tabular data synthesis method with innovative distributed marginal selection techniques for the first time.

Findings

Achieves utility comparable to centralized synthesis despite federated noise.

Effective in range queries, Wasserstein fidelity, and machine learning tasks.

Introduces new dependency metrics and adaptive strategies for distributed data synthesis.

Abstract

Traditional Differential Privacy (DP) mechanisms are typically tailored to specific analysis tasks, which limits the reusability of protected data. DP tabular data synthesis overcomes this by generating synthetic datasets that can be shared for arbitrary downstream tasks. However, existing synthesis methods predominantly assume centralized or local settings and overlook the more practical horizontal federated scenario. Naively synthesizing data locally or perturbing individual records either produces biased mixtures or introduces excessive noise, especially under heterogeneous data distributions across participants. We propose HeteroFedSyn, the first DP tabular data synthesis framework designed specifically for the horizontal federated setting. Built upon the PrivSyn paradigm of 2-way marginal-based synthesis, HeteroFedSyn introduces three key innovations for distributed marginal selection: (i) an L2-based dependency metric with random projection for noise-efficient correlation measurement, (ii) an unbiased estimator to correct multiplicative noise, and (iii) an adaptive selection strategy that dynamically updates dependency scores to avoid redundancy. Extensive experiments on range queries, Wasserstein fidelity, and machine learning tasks show that, despite the increased noise inherent to federated execution, HeteroFedSyn achieves utility comparable to centralized synthesis. Our code is open-sourced via the link.