Privacy Preserving Data Mining Using Association Rule Based on Apriori Algorithm

Abstract

Data mining is a process of extracting knowledge from the large databases. This has made data mining a significant and functional emerging trend. Association rule is one of the most used data mining techniques that discover hidden correlations from huge data sets. There are several mining algorithms for association rules Apriori is one of the most popular algorithm used for extracting frequent item sets from databases and getting the association rule for knowledge discovery. The time required for generating frequent item sets plays an important role. Based on this algorithm we are performing comparison of sanitized data and existing data based on number of iterations and the execution time. The experimental results shows that the number of iteration is reduced in sanitized data than that of existing data also the time is reduced in sanitized data. The association rule generation leads to ensure privacy of the dataset by creating items so, in this way privacy of association rules along with data quality is well maintained.