作者
Markus Hittmeir, Andreas Ekelhart, Rudolf Mayer
发表日期
2019/12/9
研讨会论文
2019 IEEE International Conference on Big Data (Big Data)
页码范围
5763-5772
出版商
IEEE
简介
With ever increasing capacity for collecting, storing, and processing of data, there is also a high demand for intelligent data analysis methods. While there have been impressive advances in machine learning and similar domains in recent years, this also gives rise to concerns regarding the protection of personal and otherwise sensitive data, especially if it is to be analysed by third parties. Besides anonymisation, which becomes challenging with high dimensional data, one approach for privacy-preserving data mining lies in the usage of synthetic data, which comes with the promise of protecting the users’ data and producing analysis results close to those achieved by using real data. In this paper, we analyse a number of different approaches for creating synthetic data, and study the utility of the created datasets for regression tasks, i.e. the prediction of a numeric value. We further investigate the similarity of real and …
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M Hittmeir, A Ekelhart, R Mayer - 2019 IEEE International Conference on Big Data (Big …, 2019