Sharing confidential data for algorithm development by multiple imputation

Authors:
Sicco Verwer;Susan van den Braak;Sunil Choenni
Affiliations:
Radboud University Nijmegen, The Netherlands;Research and Documentation Centre, Ministry of Security and Justice, the Netherlands;Research and Documentation Centre, Ministry of Security and Justice, the Netherlands
Venue:
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Year:
2013

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Abstract

The availability of real-life data sets is of crucial importance for algorithm and application development, as these often require insight into the specific properties of the data. Often, however, such data are not released because of their proprietary and confidential nature. We propose to solve this problem using the statistical technique of multiple imputation, which is used as a powerful method for generating realistic synthetic data sets. Additionally, it is shown how the generated records can be combined into networked data using clustering techniques.