Evaluating Clustering Performance of K-Anonymity Methods and Techniques in PPDM

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Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Data Management
Format: PDF
Data mining is the process of extracting hidden information from the database. The current trend in business collaboration shares the data and mine results to gain mutual benefit. Privacy preserving data mining has become increasingly popular because it allows sharing of private sensitive data for analysis purposes. K-anonymity is a property that models the protection of released data against possible re-identification of the respondents to which the data refers. This paper has introduced a new k-anonymity algorithm which is capable of transforming a non anonymous data set into a k-anonymity data set.
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