# Privacy-preserving data sharing via probabilistic modelling

arXiv: https://arxiv.org/abs/1912.04439 · HTML: https://mlsanity.com/paper/1912.04439/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2019.

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

- [Diffusion Models for Tabular Data Imputation and Synthetic Data Generation](https://mlsanity.com/paper/2407.02549/) (2024)
- [Noise-Aware Statistical Inference with Differentially Private Synthetic Data](https://mlsanity.com/paper/2205.14485/) (2022)
- [D3p -- A Python Package for Differentially-Private Probabilistic Programming](https://mlsanity.com/paper/2103.11648/) (2021)
- [DPVIm: Differentially Private Variational Inference Improved](https://mlsanity.com/paper/2210.15961/) (2022)
- [A Bias-Variance Decomposition for Ensembles over Multiple Synthetic Datasets](https://mlsanity.com/paper/2402.03985/) (2024)
- [Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data](https://mlsanity.com/paper/2308.04755/) (2023)
- [Machine Learning for Network Attacks Classification and Statistical Evaluation of Adversarial Learning Methodologies for Synthetic Data Generation](https://mlsanity.com/paper/2603.17717/) (2026)

## Abstract (arXiv)

Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation, but would leave open the problem of designing what kind of synthetic data. We propose formulating the problem of private data release through probabilistic modelling. This approach transforms the problem of designing the synthetic data into choosing a model for the data, allowing also including prior knowledge, which improves the quality of the synthetic data. We demonstrate empirically, in an epidemiological study, that statistical discoveries can be reliably reproduced from the synthetic data. We expect the method to have broad use in creating high-quality anonymized data twins of key data sets for research.