# Synthetic Data for Social Good

arXiv: https://arxiv.org/abs/1710.08874 · HTML: https://mlsanity.com/paper/1710.08874/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2017.

## Built on by (most cited first, 15 of 15)

- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Best Practices and Lessons Learned on Synthetic Data](https://mlsanity.com/paper/2404.07503/) (2024)
- [Self-Taught Evaluators](https://mlsanity.com/paper/2408.02666/) (2024)
- [Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data](https://mlsanity.com/paper/2310.16524/) (2023)
- [Fidelity and Privacy of Synthetic Medical Data](https://mlsanity.com/paper/2101.08658/) (2021)
- [Perspectives on Incorporating Expert Feedback into Model Updates](https://mlsanity.com/paper/2205.06905/) (2022)
- [Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework](https://mlsanity.com/paper/2504.01908/) (2025)
- [WheelPose: Data Synthesis Techniques to Improve Pose Estimation Performance on Wheelchair Users](https://mlsanity.com/paper/2404.17063/) (2024)
- [Improving Correlation Capture in Generating Imbalanced Data using Differentially Private Conditional GANs](https://mlsanity.com/paper/2206.13787/) (2022)
- [Synthetic CVs To Build and Test Fairness-Aware Hiring Tools](https://mlsanity.com/paper/2508.21179/) (2025)
- [Holdout-Based Fidelity and Privacy Assessment of Mixed-Type Synthetic Data](https://mlsanity.com/paper/2104.00635/) (2021)
- [RISC: Generating Realistic Synthetic Bilingual Insurance Contract](https://mlsanity.com/paper/2304.04212/) (2023)
- [Privacy-Preserving Dataset Combination](https://mlsanity.com/paper/2502.05765/) (2025)
- [Advancing Employee Behavior Analysis through Synthetic Data: Leveraging ABMs, GANs, and Statistical Models for Enhanced Organizational Efficiency](https://mlsanity.com/paper/2409.14197/) (2024)
- [Fragments to Facts: Partial-Information Fragment Inference from LLMs](https://mlsanity.com/paper/2505.13819/) (2025)

## Abstract (arXiv)

Data for good implies unfettered access to data. But data owners must be conservative about how, when, and why they share data or risk violating the trust of the people they aim to help, losing their funding, or breaking the law. Data sharing agreements can help prevent privacy violations, but require a level of specificity that is premature during preliminary discussions, and can take over a year to establish. We consider the generation and use of synthetic data to facilitate ad hoc collaborations involving sensitive data. A good synthetic dataset has two properties: it is representative of the original data, and it provides strong guarantees about privacy. In this paper, we discuss important use cases for synthetic data that challenge the state of the art in privacy-preserving data generation, and describe DataSynthesizer, a dataset generation tool that takes a sensitive dataset as input and generates a structurally and statistically similar synthetic dataset, with strong privacy guarantees, as output. The data owners need not release their data, while potential collaborators can begin developing models and methods with some confidence that their results will work similarly on the real dataset. The distinguishing feature of DataSynthesizer is its usability - in most cases, the data owner need not specify any parameters to start generating and sharing data safely and effectively. The code implementing DataSynthesizer is publicly available on GitHub at https://github.com/DataResponsibly. The work on DataSynthesizer is part of the Data, Responsibly project, where the goal is to operationalize responsibility in data sharing, integration, analysis and use.