Differentially private feature selection

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.