Evaluation of Synthetic Data for Privacy-Preserving Machine Learning

被引：0
|
作者：
Hittmeir, Markus [1 ]
Ekelhart, Andreas [1 ]
Mayer, Rudolf [1 ]
机构：
[1] SBA Res, Vienna, Austria
来源：
ERCIM NEWS | 2020年 / 123期
关键词：
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The generation of synthetic data is widely considered to be an effective way of ensuring privacy and reducing the risk of disclosing sensitive information in micro-data. We analysed these risks and the utility of synthetic data for machine learning tasks. Our results demonstrate the suitability of this approach for privacy-preserving data publishing.
引用
收藏
页码：30 / 31
页数：2
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