Privacy Preserving Classification of Heterogeneous Partition Data Through ID3 Technique
The goal of data mining is to extract or mine knowledge from large amounts of data. For information Extraction this knowledge several data mining classification techniques are used. ID3 algorithm is widely used technique in this classification arena. ID3 Algorithm classifies data by creating decision tree over heterogeneously partitioned data. In this paper, the authors propose vertically partitioned micro array data along with preserving privacy by different methods of privacy preserving i.e. secure multi party computation However, micro data is often collected by several different sites. Privacy, legal and commercial concerns restrict centralized access to this data. Together, these enable the secure mining of knowledge. They focus on the problem of decision tree learning with the popular ID3 algorithm.