# A new PCA-based utility measure for synthetic data evaluation

Source: [A new PCA-based utility measure for synthetic data evaluation](<https://www.sota2.com/research/paper/68fa6e301e209131cea3e70c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:19:28.634Z

Authors: F. K. Dankar, M. K. Ibrahim

Published: 2022-11-26T20:24:15.000Z

## Abstract

Data synthesis is a privacy enhancing technology aiming to produce realistic and timely data when real data is hard to obtain. Utility of synthetic data generators (SDGs) has been investigated through different utility metrics. These metrics have been found to generate conflicting conclusions making direct comparison of SDGs surprisingly difficult. Moreover, prior research found no correlation between popular metrics, concluding they tackle different utility-dimensions. This paper aggregates four popular utility metrics (representing different utility dimensions) into one using principal-component-analysis and checks whether the new measure can generate synthetic data that perform well in real-life. The new measure is used to compare four well-recognized SDGs.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2212.05595>)

[PDF](<https://arxiv.org/pdf/2212.05595.pdf>)