# Generating Artificial Data for Private Deep Learning

arXiv: https://arxiv.org/abs/1803.03148 · HTML: https://mlsanity.com/paper/1803.03148/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 9 of 9)

- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [Improving Fractal Pre-training](https://mlsanity.com/paper/2110.03091/) (2021)
- [A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions](https://mlsanity.com/paper/2406.03820/) (2024)
- [Ensembles of GANs for synthetic training data generation](https://mlsanity.com/paper/2104.11797/) (2021)
- [A Unified View of Differentially Private Deep Generative Modeling](https://mlsanity.com/paper/2309.15696/) (2023)
- [Preserving Privacy in Personalized Models for Distributed Mobile Services](https://mlsanity.com/paper/2101.05855/) (2021)
- [Preserving Privacy in GANs Against Membership Inference Attack](https://mlsanity.com/paper/2311.03172/) (2023)
- [Synthetic Dataset Generation for Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2210.03205/) (2022)

## Abstract (arXiv)

In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy with respect to the original dataset. We use generative adversarial network to draw privacy-preserving artificial data samples and derive an empirical method to assess the risk of information disclosure in a differential-privacy-like way. Our experiments show that we are able to generate artificial data of high quality and successfully train and validate machine learning models on this data while limiting potential privacy loss.