Differentially Private Synthetic Data Generation for Relational Databases
Name
alimohammadi-mrz-sm-idss-2025-thesis.pdf
Description
Thesis PDF
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1.2 MB
Format
Adobe PDF
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866a8ddb9e96a6a1e26d188ab9fb0364
Author(s)
Alimohammadi, Kaveh
Advisor(s)
Azizan, Navid
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
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. This study presents the first-of-its-kind algorithm that can be combined with \emph{any} existing DP mechanisms to generate synthetic relational databases. The 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; consequently eliminates the need to flatten a relational database into a master table (saving space), operates efficiently (saving time), and scales effectively to high-dimensional data. We provide both DP and theoretical utility guarantees for our algorithm. Through numerical experiments on real-world datasets, we demonstrate the effectiveness of our method in preserving fidelity to the original data.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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