Title :
Differentially private feature selection
Author :
Jun Yang ; Yun Li
Author_Institution :
Coll. of Comput. Sci., Nanjing Univ. of Posts & Telecommun., Nanjing, China
Abstract :
The privacy-preserving data analysis has been gained significant interest across several research communities. The current researches mainly focus on privacy-preserving classification and regression. However, feature selection is also an essential component for data analysis, which can be used to reduce the data dimensionality and can be utilized to discover knowledge, such as inherent variables in data. In this paper, in order to efficiently mine sensitive data, a privacy preserving feature selection algorithm is proposed and analyzed in theory based on local learning and differential privacy. We also conduct some experiments on benchmark data sets. The Experimental results show that our algorithm can preserve the data privacy to some extent.
Keywords :
data analysis; data mining; data privacy; learning (artificial intelligence); data dimensionality reduction; data mining; data privacy; differential privacy; differentially private feature selection; feature selection; knowledge discovery; local learning; privacy preserving feature selection algorithm; privacy-preserving classification; privacy-preserving data analysis; privacy-preserving regression; Accuracy; Algorithm design and analysis; Computational modeling; Data privacy; Logistics; Privacy; Vectors;
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
DOI :
10.1109/IJCNN.2014.6889613
