DISTRIBUTION OF DATA BASED ON DEPENDENCIES AND ASSOCIATION RULES

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Today's massive databases provide a daunting challenge. But it's possible that conventional data analytics won't be able to deal with these massive volumes. As a result, researchers exert considerable effort to provide a conducive setting in which to analyse and improve the algorithms. This study introduces a technique for safeguarding digital data by examining the relationship between the columns of a dataset using highly relative connection patterns. When columns are relocated to other positions, the resulting dataset is reduced in size. Therefore, a sophisticated encryption technique was implemented to increase the security of information kept on these servers. The adult dataset is a large collection of real-world data utilised in scientific research. The proposed technique is superior to current approaches, as shown by the comparative results. On the basis of assessment criteria, outperforms other, more conventional methods.

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Keywords

Effective Pruning, Data Mining, Functional Dependency, Encryption, Distributed Data.