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Attribute Segregation based on Feature Ranking Framework for Privacy Preserving Data Mining
2015
Indian Journal of Science and Technology
Attributes in macro-data have to be segregating based on their sensitivity for privacy preservation purposes. Automating this attribute segregation becomes complicated in high dimensional datasets and data streams. In this work, information or correlation of the attribute on the target class attribute is measured using Information Gain [IG], Gain Ratio [GR] and Pearson Correlation [PC] ranker based feature selection methods and this values are used to segregate them as Sensitive Attributes
doi:10.17485/ijst/2015/v8i17/77584
fatcat:3h4b4g6zjzdihmrcmr5fqldb6y