SynDiffix: More accurate synthetic structured data

TL;DR

SynDiffix is a novel method for generating highly accurate, anonymous synthetic structured data by leveraging traditional aggregation and noise techniques, outperforming many existing models in accuracy and speed.

Contribution

Introduces SynDiffix, a new approach that uses traditional mechanisms for synthetic data generation, achieving superior accuracy and efficiency over existing GAN-based and autoencoder methods.

Findings

SynDiffix generates data twice as accurate as CTGAN.

It is 10-100 times more accurate for marginal and column pair data.

Execution time is two orders of magnitude faster than comparable systems.

Abstract

This paper introduces SynDiffix, a mechanism for generating statistically accurate, anonymous synthetic data for structured data. Recent open source and commercial systems use Generative Adversarial Networks or Transformed Auto Encoders to synthesize data, and achieve anonymity through overfitting-avoidance. By contrast, SynDiffix exploits traditional mechanisms of aggregation, noise addition, and suppression among others. Compared to CTGAN, ML models generated from SynDiffix are twice as accurate, marginal and column pairs data quality is one to two orders of magnitude more accurate, and execution time is two orders of magnitude faster. Compared to the best commercial product we measured (MostlyAI), ML model accuracy is comparable, marginal and pairs accuracy is 5 to 10 times better, and execution time is an order of magnitude faster. Similar to the other approaches, SynDiffix anonymization is very strong. This paper describes SynDiffix and compares its performance with other popular open source and commercial systems.