  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.
