Privacy-Enhanced Database Synthesis for Benchmark Publishing

• URL: http://arxiv.org/abs/2405.01312v1
• Date: Thu, 2 May 2024 14:20:24 GMT
• Title: Privacy-Enhanced Database Synthesis for Benchmark Publishing
• Authors: Yongrui Zhong, Yunqing Ge, Jianbin Qin, Shuyuan Zheng, Bo Tang, Yu-Xuan Qiu, Rui Mao, Ye Yuan, Makoto Onizuka, Chuan Xiao,
• Abstract summary: Differential privacy has become a key method for safeguarding privacy when sharing data, but the focus has largely been on minimizing errors in aggregate queries or classification tasks. This paper delves into the creation of privacy-preserving databases specifically for benchmarking, aiming to produce a differentially private database. PrivBench uses sum-product networks (SPNs) to partition and sample data, enhancing data representation while securing privacy.
• Score: 16.807486872855534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 has become a key method for safeguarding privacy when sharing data, but the focus has largely been on minimizing errors in aggregate queries or classification tasks, with less attention given to benchmarking factors like runtime performance. This paper delves into the creation of privacy-preserving databases specifically for benchmarking, aiming to produce a differentially private database whose query performance closely resembles that of the original data. Introducing PrivBench, an innovative synthesis framework, we support the generation of high-quality data that maintains privacy. PrivBench uses sum-product networks (SPNs) to partition and sample data, enhancing data representation while securing privacy. The framework allows users to adjust the detail of SPN partitions and privacy settings, crucial for customizing privacy levels. We validate our approach, which uses the Laplace and exponential mechanisms, in maintaining privacy. Our tests show that PrivBench effectively generates data that maintains privacy and excels in query performance, consistently reducing errors in query execution time, query cardinality, and KL divergence.

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