@inproceedings{d92368e50fb84684b74ddbabf3b54e6a,
title = "Empirical evaluation on synthetic data generation with generative adversarial network",
abstract = "Data release has been proven to be impactful in scientific research and business innovation. Nevertheless, the valuable data often contains personal information so that the data release also leads to privacy leakage. Releasing a synthetic data may be a solution for the problem of private data release. In this paper, we consider a generative adversarial networks (GAN)-based synthetic data generation. Furthermore, we perform extensive experiments to evaluate the data utility and risk of re-identification of our GAN-based solution.",
keywords = "Data release, Generative adversarial nework, Synthetic dataset",
author = "Lu, {Pei Hsuan} and Wang, {Pang Chieh} and Yu, {Chia Mu}",
note = "Publisher Copyright: {\textcopyright} 2019 ACM.; 9th International Conference on Web Intelligence, Mining and Semantics, WIMS 2019 ; Conference date: 26-06-2019 Through 28-06-2019",
year = "2019",
month = jun,
day = "26",
doi = "10.1145/3326467.3326474",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics, WIMS 2019",
}