TBM, a transformation based method for microaggregation of large volume mixed data

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• 作者：Mostafa Salari ; Saeed Jalili ; Reza Mortazavi
• 关键词：Microaggregation ; Large mixed data ; k ; anonymity ; Privacy Preserving Data Publishing ; Multidimensional scaling
• 刊名：Data Mining and Knowledge Discovery
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：31
• 期：1
• 页码：65-91
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Data Mining and Knowledge Discovery; Artificial Intelligence (incl. Robotics); Information Storage and Retrieval; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences;
• 出版者：Springer US
• ISSN：1573-756X
• 卷排序：31

文摘

Due to recent advances in data collection and processing, data publishing has emerged by some organizations for scientific and commercial purposes. Published data should be anonymized such that staying useful while the privacy of data respondents is preserved. Microaggregation is a popular mechanism for data anonymization, but naturally operates on numerical datasets. However, the type of data in the real world is usually mixed i.e., there are both numeric and categorical attributes together. In this paper, we propose a novel transformation based method for microaggregation of mixed data called TBM. The method uses multidimensional scaling to generate a numeric equivalent from mixed dataset. The partitioning step of microaggregation is performed on the equivalent dataset but the aggregation step on the original data. TBM can microaggregate large mixed datasets in a short time with low information loss. Experimental results show that the proposed method attains better trade-off between data utility and privacy in a shorter time in comparison with the traditional methods.