# Differentially Private Synthetic High-dimensional Tabular Stream

arXiv: https://arxiv.org/abs/2409.00322 · HTML: https://mlsanity.com/paper/2409.00322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 31 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [DP-CGAN: Differentially Private Synthetic Data and Label Generation](https://mlsanity.com/paper/2001.09700/) (2020)
- [Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe](https://mlsanity.com/paper/2210.14348/) (2022)
- [Winning the NIST Contest: A scalable and general approach to differentially private synthetic data](https://mlsanity.com/paper/2108.04978/) (2021)
- [Graphical-model based estimation and inference for differential privacy](https://mlsanity.com/paper/1901.09136/) (2019)
- [Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods](https://mlsanity.com/paper/2106.07153/) (2021)
- [Differentially Private Query Release Through Adaptive Projection](https://mlsanity.com/paper/2103.06641/) (2021)
- [The Price of Differential Privacy under Continual Observation](https://mlsanity.com/paper/2112.00828/) (2021)
- [Dual Query: Practical Private Query Release for High Dimensional Data](https://mlsanity.com/paper/1402.1526/) (2014)
- [Continuous Release of Data Streams under both Centralized and Local Differential Privacy](https://mlsanity.com/paper/2005.11753/) (2020)
- [Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections](https://mlsanity.com/paper/2306.07884/) (2023)
- [Counting Distinct Elements in the Turnstile Model with Differential Privacy under Continual Observation](https://mlsanity.com/paper/2306.06723/) (2023)
- [Differentially Private Histogram, Predecessor, and Set Cardinality under Continual Observation](https://mlsanity.com/paper/2306.10428/) (2023)
- [An Algorithm for Streaming Differentially Private Data](https://mlsanity.com/paper/2401.14577/) (2024)

## Abstract (arXiv)

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.