PRESERVING PRIVACY IN DATA MINING USING SEMMA METHODOLOGY
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ABSTRACT:
The huge amount of data available means that it is possible to learn a lot of information about individuals from public data. Here, this open data need to be sheltered from unlawful contact. The privacy-preserving data mining (PPDM) has thus become a significant subject in most recent years. Generally privacy means “keep information about person from being available to others” but, the real worry is that their information not be mishandle. The data mining techniques enable users to extract the hidden patterns which may lead to leakage of sensitive data. So the main concern is to secure the data mining result with the help of PPDM. This paper provides a framework to preserve privacy in data mining results by manipulating SEMMA analysis cycle.
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