SynDiffix is a new open-source tool for structured data synthesis. It has anonymization features that allow it to generate multiple synthetic tables while maintaining strong anonymity. Compared to the more common single-table approach, multi-table leads to more accurate data, since only the features of interest for a given analysis need be synthesized. This paper compares SynDiffix with 15 other commercial and academic synthetic data techniques using the SDNIST analysis framework, modified by us to accommodate multi-table synthetic data. The results show that SynDiffix is many times more accurate than other approaches for low-dimension tables, but somewhat worse than the best single-table techniques for high-dimension tables.

翻译：SynDiffix是一款面向结构化数据合成的新型开源工具。其匿名化特性可在生成多张合成数据表的同时保持强匿名性。相比常见的单表方法，多表方法仅需合成特定分析所需的目标特征，因此能产出更精准的数据。本文基于SDNIST分析框架（我们对该框架进行了修改以适应多表合成数据场景），将SynDiffix与15种其他商业及学术合成数据技术进行对比。结果表明：在低维表格中，SynDiffix的精确度远超其他方法；但在高维表格中，其表现略逊于最优单表技术。