@inproceedings{
hoorn2023generating,
title={Generating Privacy-Preserving Longitudinal Synthetic Data},
author={Robin van Hoorn and Tom Bakkes and Zoi Tokoutsi and Ymke de Jong and R. Arthur Bouwman and Mykola Pechenizkiy},
booktitle={NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI},
year={2023},
url={https://openreview.net/forum?id=Xr13v66xxT}
}
Before synthetic data (SD) generators are able to generate entire electronic health records, many challenges still have to be tackled. One of these challenges is to generate both privacy-preserving and longitudinal SD. This research combines the research streams of longitudinal SD and privacy-preserving static SD and presents a novel GAN architecture called Time-ADS-GAN. Time-ADS-GAN outperforms current state-of-the-art models on both utility and privacy on three datasets and is able to reproduce the results of a healthcare study significantly better than TimeGAN. As a second contribution, a variation of the $\epsilon$-identifiability metric is introduced and used in the analysis.
Generating Privacy-Preserving Longitudinal Synthetic Data
Robin van Hoorn Tom Bakkes Zoi Tokoutsi Ymke de Jong R. Arthur Bouwman Mykola Pechenizkiy