# DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges

arXiv: https://arxiv.org/abs/2106.12949 · HTML: https://mlsanity.com/paper/2106.12949/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2021.

## Built on by (most cited first, 7 of 7)

- [Systematic Assessment of Tabular Data Synthesis](https://mlsanity.com/paper/2402.06806/) (2024)
- [Synthetic Data: Methods, Use Cases, and Risks](https://mlsanity.com/paper/2303.01230/) (2023)
- [Privacy-preserving data release leveraging optimal transport and particle gradient descent](https://mlsanity.com/paper/2401.17823/) (2024)
- [Relaxed Marginal Consistency for Differentially Private Query Answering](https://mlsanity.com/paper/2109.06153/) (2021)
- [HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization](https://mlsanity.com/paper/2405.08043/) (2024)
- [Differentially Private Synthetic Data via APIs 4: Tabular Data](https://mlsanity.com/paper/2606.08259/) (2026)
- [Differential Privacy in Machine Learning: A Survey from Symbolic AI to LLMs](https://mlsanity.com/paper/2506.11687/) (2025)

## Abstract (arXiv)

We summarize the experience of participating in two differential privacy competitions organized by the National Institute of Standards and Technology (NIST). In this paper, we document our experiences in the competition, the approaches we have used, the lessons we have learned, and our call to the research community to further bridge the gap between theory and practice in DP research.