Multi-interval monotonic data transformation for privacy-preserving data mining in rule-based classification

Several methods have been proposed for privacy-preserving data mining, and classification is one of the major tasks in data mining. Previous privacy-preserving methods attempted to improve classification accuracy, while model utilization is also a critical issue in classification tasks. An individual can be identified from the combination of attributes, and this study considers both the achievement of privacy preserving and the utilization of classification models to design a novel method for transforming continuous or numeric attributes. To ensure the monotonic property of continuous attributes, multi-interval monotonic functions with randomly chosen parameters are employed for data transformation. Original and transformed data were analyzed by decision tree learning and RIPPER algorithm. By comparing with a perturbation method and a transformation method proposed by previous studies, the experimental results on ten data sets showed that only our method can achieve both privacy preserving and model utilization. The privacy of individuals can be enhanced by increasing the number of intervals for monotonic transformation.