Tunable correlation retention: A statistical method for generating synthetic data

TL;DR

This paper introduces a statistical method for generating synthetic data that preserves feature correlations and offers tunable privacy levels by controlling the depth of conditional distributions used.

Contribution

The paper presents a novel, flexible approach to synthetic data generation that maintains inter-feature correlations and allows adjustable privacy settings through a tunable parameter.

Findings

Method accurately reproduces correlation structures in synthetic data.

Tunable parameters effectively balance data fidelity and privacy.

Applicable to various datasets, including real-world energy data.

Abstract

We propose a method to generate statistically representative synthetic data from a given dataset. The main goal of our method is for the created data set to mimic the inter--feature correlations present in the original data, while also offering a tunable parameter to influence the privacy level. In particular, our method constructs a statistical map by using the empirical conditional distributions between the features of the original dataset. Part of the tunability is achieved by limiting the depths of conditional distributions that are being used. We describe in detail our algorithms used both in the construction of a statistical map and how to use this map to generate synthetic observations. This approach is tested in three different ways: with a hand calculated example; a manufactured dataset; and a real world energy-related dataset of consumption/production of households in Madeira Island. We evaluate the method by comparing the datasets using the Pearson correlation matrix with different levels of resolution and depths of correlation. These two considerations are being viewed as tunable parameters influencing the resulting datasets fidelity and privacy. The proposed methodology is general in the sense that it does not rely on the used test dataset. We expect it to be applicable in a much broader context than indicated here.