DEREC-SIMPRO: unlock Language Model benefits to advance Synthesis in Data Clean Room

TL;DR

This paper introduces DEREC-SIMPRO, a pipeline and evaluation metrics that enhance multi-table synthetic data generation for data collaboration, addressing privacy concerns and repeated subject issues.

Contribution

It proposes a novel DEREC pre-processing pipeline and SIMPRO evaluation metrics to improve multi-table synthesizers' performance in data collaboration scenarios.

Findings

DEREC improves synthetic data fidelity.

Multi-table synthesizers outperform single-table methods.

The pipeline enables better data collaboration.

Abstract

Data collaboration via Data Clean Room offers value but raises privacy concerns, which can be addressed through synthetic data and multi-table synthesizers. Common multi-table synthesizers fail to perform when subjects occur repeatedly in both tables. This is an urgent yet unresolved problem, since having both tables with repeating subjects is common. To improve performance in this scenario, we present the DEREC 3-step pre-processing pipeline to generalize adaptability of multi-table synthesizers. We also introduce the SIMPRO 3-aspect evaluation metrics, which leverage conditional distribution and large-scale simultaneous hypothesis testing to provide comprehensive feedback on synthetic data fidelity at both column and table levels. Results show that using DEREC improves fidelity, and multi-table synthesizers outperform single-table counterparts in collaboration settings. Together, the DEREC-SIMPRO pipeline offers a robust solution for generalizing data collaboration, promoting a more efficient, data-driven society.