Adapting Differentially Private Synthetic Data to Relational Databases

Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with relationships across tables. In this paper, we introduce the first-of-its-kind algorithm that can be combined with any...

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Bibliographic Details
Main Authors Alimohammadi, Kaveh, Wang, Hao, Gulati, Ojas, Srivastava, Akash, Azizan, Navid
Format Journal Article
LanguageEnglish
Published 28.05.2024
Subjects
Computer Science - Cryptography and Security
Computer Science - Databases
Computer Science - Learning
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Summary:Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with relationships across tables. In this paper, we introduce the first-of-its-kind algorithm that can be combined with any existing DP mechanisms to generate synthetic relational databases. Our algorithm iteratively refines the relationship between individual synthetic tables to minimize their approximation errors in terms of low-order marginal distributions while maintaining referential integrity. Finally, we provide both DP and theoretical utility guarantees for our algorithm.
DOI:10.48550/arxiv.2405.18670