Adapting Differentially Private Synthetic Data to Relational Databases

• URL: http://arxiv.org/abs/2405.18670v1
• Date: Wed, 29 May 2024 00:25:07 GMT
• Title: Adapting Differentially Private Synthetic Data to Relational Databases
• Authors: Kaveh Alimohammadi, Hao Wang, Ojas Gulati, Akash Srivastava, Navid Azizan,
• Abstract summary: We introduce the first-of-its-kind algorithm that can be combined with any existing differentially private (DP) synthetic data generation mechanisms. Our algorithm iteratively refines the relationship between individual synthetic tables to minimize their approximation errors.
• Score: 9.532509662034062
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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