Struct-Bench: A Benchmark for Evaluating Differentially Private Synthetic Data for Structured Datasets Containing Natural Language
Shuaiqi Wang, Vikas Raunak, Arturs Backurs, Victor Reis, Pei Zhou, Sihao Chen, Longqi Yang, Zinan Lin, Sergey Yekhanin, Giulia Fanti
Differentially private (DP) synthetic data generation (SDG) is a promising technique for making use of private datasets that otherwise cannot be exposed for model training or other analytics. While much research literature has focused on generating private unstructured text and image data, in enterprise settings, structured data (e.g., tabular) is more common, often including natural language fields or components. Such datasets have structural properties and correlations that are difficult to evaluate using only existing synthetic data evaluation techniques (e.g., FID). In this work, we propose Struct-Bench, a framework and benchmark for evaluating private synthetic datasets based on structured datasets that contain natural language data. The Struct-Bench framework requires users to provide a representation of their dataset structure as a Context-Free Grammar (CFG). Our benchmark comprises 5 real-world and 2 synthetically generated datasets, each annotated with Context-Free Grammars (CFGs). We show that these datasets demonstrably present a great challenge even for state-of-the-art DP synthetic data generation methods. Struct-Bench also includes reference implementations of different metrics and a leaderboard, thereby providing researchers a standardized evaluation platform to benchmark and investigate privacy-preserving synthetic data generation methods. Further, we also present a case study showing how to use Struct-Bench to improve the synthetic data quality of Private Evolution on structured data. The benchmark and the leaderboard have been publicly made available at https://struct-bench.github.io.