Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation
This work addresses a practical challenge in deploying synthetic data for secure data sharing, but it is incremental as it adapts existing methods to a new setting without major breakthroughs.
The paper tackles the problem of improving synthetic data quality in differentially private synthetic data generation by adapting methods from horizontal to vertical public-private data splits, where datasets have public and private attributes, and identifies limitations of current approaches.
Differentially Private Synthetic Data Generation (DP-SDG) is a key enabler of private and secure tabular-data sharing, producing artificial data that carries through the underlying statistical properties of the input data. This typically involves adding carefully calibrated statistical noise to guarantee individual privacy, at the cost of synthetic data quality. Recent literature has explored scenarios where a small amount of public data is used to help enhance the quality of synthetic data. These methods study a horizontal public-private partitioning which assumes access to a small number of public rows that can be used for model initialization, providing a small utility gain. However, realistic datasets often naturally consist of public and private attributes, making a vertical public-private partitioning relevant for practical synthetic data deployments. We propose a novel framework that adapts horizontal public-assisted methods into the vertical setting. We compare this framework against our alternative approach that uses conditional generation, highlighting initial limitations of public-data assisted methods and proposing future research directions to address these challenges.