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ID3 decision tree data mining is a popular and widely studied data analysis technique for a range of applications. In this paper, we focus on the privacy-preserving ID3 decision tree algorithm on horizontally partitioned datasets. In such a scenario, data owners wish to learn the decision tree result from a collective data set but disclose minimal information about their own sensitive data. In this...
Within the context of privacy preserving data mining, several solutions for privacy-preserving classification rules learning such as association rules mining have been proposed. Each solution was provided for horizontally or vertically distributed scenario. The aim of this work is to study privacy-preserving classification rules learning in two-dimension distributed data, which is a generalisation...
Privacy has in recent times become an astounding akin to an oxymoron. It can either be embellished or marred with technology; confiscating more consideration in many data mining applications. We are focusing on information safety measures in order to preserve the individual's privacy, so that no personal information can be gained by the hacker from the data. Under the modern state of affairs of technological...
Privacy is the most important apprehension in many data mining applications. In this paper a new technique called Cryptic Random Projection, solves the re-identification quandary (which is found in the conventional random projections).Here this encryption based random projection assigns secret keys to the positions of random matrix elements and not to the random numbers. We have addressed two kinds...
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