Differentially private attribute selection for classification
[ X ]
Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Gazi Univ, Fac Engineering Architecture
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Selecting a relevant subset of attributes is one of the most important data preprocessing steps of data mining and machine learning solutions. For the classification task, selection is based on the correlation between an attribute and the class attribute. There are various studies on privacy preserving classification. However, there is no attribute selection solution for such work in the literature. In this study, novel attribute selection methods based on the state of the art solution in statistical database security, known as differential privacy, are proposed. The proposed solutions are implemented with the popular data mining library WEKA and experimental results confirm the positive effects of the proposed solutions on classification accuracy.
Açıklama
Anahtar Kelimeler
Differential privacy, classification, attribute selection
Kaynak
Journal of The Faculty of Engineering and Architecture of Gazi University
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
33
Sayı
1
