A Review Approaches for Hiding Sensitive Association Rules in Data Mining
DOI:
https://doi.org/10.26438/ijcse/v6i11.920924Keywords:
Data Mining, Association rule mining, privacy preserving data mining (PPDM)Abstract
Nowadays, Data Mining is a popular tool for extracting hidden knowledge from huge amount of data. To find hidden knowledge in the data without revealing sensitive information is one of the major challenges in data mining. There are many strategies have been proposed to hide the sensitive information. Association rule mining is one of the data mining techniques used to extract hidden knowledge from large datasets. This hidden knowledge contains most of the times confidential information that the users want to keep private or do not want to disclose to public. Therefore, privacy preserving data mining (PPDM) techniques are used to preserve such confidential information or restrictive pattern from unauthorized access. In this paper, all the approaches for hiding sensitive association rules in PPDM have been compared theoretically and points out their pros and cons
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