Abstract
Generating synthetic datasets is an innovative approach for data dissemination. Values at risk of disclosure or even the entire dataset are replaced with multiple draws from statistical models. The quality of the released data strongly depends on the ability of these models to capture important relationships found in the original data. Defining useful models for complex survey data can be difficult and cumbersome. One possible approach to reduce the modeling burden for data disseminating agencies is to rely on machine learning tools to reveal important relationships in the data.
This paper contains an initial investigation to evaluate whether support vector machines could be utilized to develop synthetic datasets. The application is limited to categorical data but extensions for continuous data should be straight forward. I briefly describe the concept of support vector machines and necessary adjustments for synthetic data generation. I evaluate the performance of the suggested algorithm using a real dataset, the IAB Establishment Panel. The results indicate that some data utility improvements might be achievable using support vector machines. However, these improvements come at the price of an increased disclosure risk compared to standard parametric modeling and more research is needed to find ways for reducing the risk. Some ideas for achieving this goal are provided in the discussion at the end of the paper.
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Drechsler, J. (2010). Using Support Vector Machines for Generating Synthetic Datasets. In: Domingo-Ferrer, J., Magkos, E. (eds) Privacy in Statistical Databases. PSD 2010. Lecture Notes in Computer Science, vol 6344. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15838-4_14
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