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

页码：30 / 31

页数：2

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