Privacy-Enhanced Database Synthesis for Benchmark Publishing

Yunqing Ge, Jianbin Qin, Shuyuan Zheng*, Yongrui Zhong, Bo Tang, Yu Xuan Qiu, Rui Mao, Ye Yuan, Makoto Onizuka, Chuan Xiao

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Benchmarking is crucial for evaluating a DBMS, yet existing benchmarks often fail to reflect the varied nature of user workloads. As a result, there is increasing momentum toward creating databases that incorporate real-world user data to more accurately mirror business environments. However, privacy concerns deter users from directly sharing their data, underscoring the importance of creating synthesized databases for benchmarking that also prioritize privacy protection. Differential privacy (DP)-based data synthesis has become a key method for safeguarding privacy when sharing data, but the focus has largely been on minimizing errors in aggregate queries or downstream ML tasks, with less attention given to benchmarking factors like query runtime performance. This paper delves into differentially private database synthesis specifically for benchmark publishing scenarios, aiming to produce a synthetic database whose benchmarking factors closely resemble those of the original data. Introducing PrivBench, an innovative synthesis framework based on sum-product networks (SPNs), we support the synthesis of high-quality benchmark databases that maintain fidelity in both data distribution and query runtime performance while preserving privacy. We validate that PrivBench can ensure database-level DP even when generating multi-relation databases with complex reference relationships. Our extensive experiments show that PrivBench efficiently synthesizes data that maintains privacy and excels in both data distribution similarity and query runtime similarity.

Original languageEnglish
Pages (from-to)413-425
Number of pages13
JournalProceedings of the VLDB Endowment
Volume18
Issue number2
DOIs
Publication statusPublished - 2025
Event51st International Conference on Very Large Data Bases, VLDB 2025 - London, United Kingdom
Duration: 1 Sept 20255 Sept 2025

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