关键词
data mining
privacy-preserving data mining
k-anonymity
naïve bayes
privacy-preserving data mining
k-anonymity
naïve bayes
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J. Paranthaman. (2013). Performance Evaluation of K-Anonymized Data. 全球计算机科学与技术杂志, 13(C8), 7–11. 取读于 从 https://gjcst.com/index.php/gjcst/article/view/1367
摘要
Data mining provides tools to convert a large amount of knowledge data which is user relevant But this process could return individual s sensitive information compromising their privacy rights So based on different approaches many privacy protection mechanism incorporated data mining techniques were developed A widely used micro data protection concept is k-anonymity proposed to capture the protection of a micro data table regarding re-identification of respondents which the data refers to In this paper the effect of the anonymization due to k-anonymity on the data mining classifiers is investigated Na ve Bayes classifier is used for evaluating the anonymized and non-anonymized data
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