# Comparative Study of Differentially Private Data Synthesis Methods

arXiv: https://arxiv.org/abs/1602.01063 · HTML: https://mlsanity.com/paper/1602.01063/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2016.

## Built on by (most cited first, 13 of 13)

- [Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data](https://mlsanity.com/paper/2403.07797/) (2024)
- [Distribution-Invariant Differential Privacy](https://mlsanity.com/paper/2111.05791/) (2021)
- [Tabular Data Synthesis with Differential Privacy: A Survey](https://mlsanity.com/paper/2411.03351/) (2024)
- [Does Differentially Private Synthetic Data Lead to Synthetic Discoveries?](https://mlsanity.com/paper/2403.13612/) (2024)
- [A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning](https://mlsanity.com/paper/2307.13127/) (2023)
- [Spending Privacy Budget Fairly and Wisely](https://mlsanity.com/paper/2204.12903/) (2022)
- [Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records](https://mlsanity.com/paper/2302.05787/) (2023)
- [HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization](https://mlsanity.com/paper/2405.08043/) (2024)
- [Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data](https://mlsanity.com/paper/2504.21634/) (2025)
- [DPpack: An R Package for Differentially Private Statistical Analysis and Machine Learning](https://mlsanity.com/paper/2309.10965/) (2023)
- [SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AI](https://mlsanity.com/paper/2411.09178/) (2024)
- [Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration](https://mlsanity.com/paper/2607.08122/) (2026)
- [PEARL: A Task-Aware Framework for Evaluating Differentially Private Synthetic Educational Data](https://mlsanity.com/paper/2609.10612/) (2026)

## Abstract (arXiv)

When sharing data among researchers or releasing data for public use, there is a risk of exposing sensitive information of individuals in the data set. Data synthesis (DS) is a statistical disclosure limitation technique for releasing synthetic data sets with pseudo individual records. Traditional DS techniques often rely on strong assumptions of a data intruder's behaviors and background knowledge to assess disclosure risk. Differential privacy (DP) formulates a theoretical approach for a strong and robust privacy guarantee in data release without having to model intruders' behaviors. Efforts have been made aiming to incorporate the DP concept in the DS process. In this paper, we examine current DIfferentially Private Data Synthesis (DIPS) techniques for releasing individual-level surrogate data for the original data, compare the techniques conceptually, and evaluate the statistical utility and inferential properties of the synthetic data via each DIPS technique through extensive simulation studies. Our work sheds light on the practical feasibility and utility of the various DIPS approaches, and suggests future research directions for DIPS.