Differentially Private Synthetic High-dimensional Tabular Stream
This work addresses a gap in maintaining privacy for dynamic data streams, which is incremental as it builds on existing offline methods.
The paper tackles the problem of updating differentially private synthetic data over time as underlying private data changes, proposing a streaming algorithm that generates multiple synthetic datasets and satisfies continual differential privacy for high-dimensional tabular data, with utility demonstrated through experiments on real-world datasets.
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.