论文标题
一种新的基于PCA的综合数据评估措施
A new PCA-based utility measure for synthetic data evaluation
论文作者
论文摘要
数据合成是一种隐私增强技术，目的是在难以获得真实数据时生成现实而及时的数据。合成数据生成器（SDG）的效用已通过不同的效用指标进行了研究。已经发现这些指标会产生矛盾的结论，从而直接比较可持续发展目标了。此外，先前的研究发现，流行指标之间没有相关性，得出结论，它们解决了不同的公用事业二维。本文将四个流行的公用事业指标（代表不同的实用程序维度）汇总为一个使用主组分 - 分析，并检查新措施是否可以生成在现实生活中表现良好的合成数据。该新措施用于比较四个公认的可持续发展目标。
Data synthesis is a privacy enhancing technology aiming to produce realistic and timely data when real data is hard to obtain. Utility of synthetic data generators (SDGs) has been investigated through different utility metrics. These metrics have been found to generate conflicting conclusions making direct comparison of SDGs surprisingly difficult. Moreover, prior research found no correlation between popular metrics, concluding they tackle different utility-dimensions. This paper aggregates four popular utility metrics (representing different utility dimensions) into one using principal-component-analysis and checks whether the new measure can generate synthetic data that perform well in real-life. The new measure is used to compare four well-recognized SDGs.
