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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Accurate Synthetic Generation of Realistic Personal Information
verfasst von : Peter Christen, Agus Pudjijono
Erschienen in: Advances in Knowledge Discovery and Data Mining
Verlag: Springer Berlin Heidelberg
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A large portion of data collected by many organisations today is about people, and often contains personal identifying information, such as names and addresses. Privacy and confidentiality are of great concern when such data is being shared between organisations or made publicly available. Research in (privacy-preserving) data mining and data linkage is suffering from a lack of publicly available real-world data sets that contain personal information, and therefore experimental evaluations can be difficult to conduct. In order to overcome this problem, we have developed a data generator that allows flexible creation of synthetic data containing personal information with realistic characteristics, such as frequency distributions, attribute dependencies, and error probabilities. Our generator significantly improves earlier approaches, and allows the generation of data for individuals, families and households.