DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges
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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.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jun 2021.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 3; 2025: 1; 2026: 1.
7 papers.
- 2024Systematic Assessment of Tabular Data Synthesiscited by 18
- 2023Synthetic Data: Methods, Use Cases, and Riskscited by 10
- 2024Privacy-preserving data release leveraging optimal transport and particle gradient descentcited by 8
- 2021Relaxed Marginal Consistency for Differentially Private Query Answeringcited by 6
- 2024HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesizationcited by 2
- 2026Differentially Private Synthetic Data via APIs 4: Tabular Datacited by 2
- 2025Differential Privacy in Machine Learning: A Survey from Symbolic AI to LLMscited by 0
- 2026Differentially Private Synthetic Data via APIs 4: Tabular Data6 Jun 2026
- 2025Differential Privacy in Machine Learning: A Survey from Symbolic AI to LLMs13 Jun 2025
- 2024HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization13 May 2024
- 2024Systematic Assessment of Tabular Data Synthesis9 Feb 2024
- 2024Privacy-preserving data release leveraging optimal transport and particle gradient descent31 Jan 2024
- 2023Synthetic Data: Methods, Use Cases, and Risks1 Mar 2023
- 2021Relaxed Marginal Consistency for Differentially Private Query Answering13 Sep 2021