A Framework for Auditable Synthetic Data Generation

TL;DR

This paper introduces a flexible framework for generating synthetic data that allows data controllers to specify and verify which statistical properties are preserved, balancing data utility and privacy in high-dimensional datasets.

Contribution

The authors propose a novel framework enabling explicit control and empirical validation of statistical properties in synthetic data generation.

Findings

Framework allows control over statistical properties

Synthetic data maintains high utility for specific tasks

Empirical validation ensures privacy constraints are met

Abstract

Synthetic data has gained significant momentum thanks to sophisticated machine learning tools that enable the synthesis of high-dimensional datasets. However, many generation techniques do not give the data controller control over what statistical patterns are captured, leading to concerns over privacy protection. While synthetic records are not linked to a particular real-world individual, they can reveal information about users indirectly which may be unacceptable for data owners. There is thus a need to empirically verify the privacy of synthetic data -- a particularly challenging task in high-dimensional data. In this paper we present a general framework for synthetic data generation that gives data controllers full control over which statistical properties the synthetic data ought to preserve, what exact information loss is acceptable, and how to quantify it. The benefits of the approach are that (1) one can generate synthetic data that results in high utility for a given task, while (2) empirically validating that only statistics considered safe by the data curator are used to generate the data. We thus show the potential for synthetic data to be an effective means of releasing confidential data safely, while retaining useful information for analysts.