A Novel Privacy Preserving Data mining using improved decision tree and KP-ABE on High Dimensional Data - Seenu, Aaluri; Kameswara Rao, M (International Journal of Engineering and Technology, 2018)
In distributed data mining environment maintaining individual data or patterns is a major issue due to high dimensionality and data size. Distributed Data mining framework can help to find the essential decision making patterns from distributed data. Privacy preserving data mining (PPDM) has emerged as a main research area for data confidentiality and knowledge sharing in between the communicating parties. As the distributed data of the individuals are stored by the third party, it leads to the misuse of distributed information in digital networks. Most of the decision patterns generated using the machine learning models for business organizations, industries and individuals has to be encoded before it is publicly shared or published. As the amount of data collected from different sources are increasing exponentially, the time taken to preserve the patterns using theÂ traditional privacy preserving data mining models also increasing due to high computational attribute selection measures and noise in the distributed data. Also, filling sparse values using the conventional models are inefficient and infeasible for privacy preserving models. In this paper, a novel privacy preserving based classification model was designed and implemented on large datasets. In this model, a filter-based privacy preserving model using improved decision tree classifier is implemented to preserve the decision patterns using IPPDM-KPABE model. Experimental results proved that the proposed model has high computational efficiency compared to the traditional privacy preserving model on high dimensional datasets.Â