Privacy-Preserving in Distributed Mining of Horizontal Partitioned Data Using DES Algorithm

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Provided by: International Journal of Computer Applications
Topic: Big Data
Format: PDF
Data mining technology has emerged as a means of identifying patterns and trends from large quantities of data. Data mining can extract important knowledge from large data collections - but sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing the data, and some types of information about the data. This paper addresses secure mining of association rules over horizontally partitioned data. The methods incorporate cryptographic techniques to minimize the information shared, while adding little overhead to the mining task.
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