Differentially Private Synthetic High-dimensional Tabular Stream

TL;DR

This paper introduces a differentially private streaming algorithm for generating and updating synthetic high-dimensional tabular data over time, ensuring privacy and utility in dynamic data environments.

Contribution

It presents a novel framework for continual differential privacy in streaming high-dimensional data, extending synthetic data generation to dynamic, real-world scenarios.

Findings

Effective privacy guarantees over data streams

High utility demonstrated on real-world datasets

Applicable to high-dimensional tabular data

Abstract

While differentially private synthetic data generation has been explored extensively in the literature, how to update this data in the future if the underlying private data changes is much less understood. We propose an algorithmic framework for streaming data that generates multiple synthetic datasets over time, tracking changes in the underlying private data. Our algorithm satisfies differential privacy for the entire input stream (continual differential privacy) and can be used for high-dimensional tabular data. Furthermore, we show the utility of our method via experiments on real-world datasets. The proposed algorithm builds upon a popular select, measure, fit, and iterate paradigm (used by offline synthetic data generation algorithms) and private counters for streams.