Local learning-based feature weighting with privacy preservation

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. On the other hand, feature selection is also one of the key problems in data mining and machine learning. However, for privacy-preserving feature selection, the relevant papers are few. In this paper, a local learning-based feature weighting framework is introduced. Moreover, in order to preserve the data privacy during local learning-based feature selection, the objective perturbation and output perturbation strategies are used to produce local learning-based feature selection algorithms with privacy preservation. Meanwhile, we give deep analysis about their privacy preserving property based on the differential privacy model. Some experiments are conducted on benchmark data sets. The experimental results show that our algorithms can preserve the data privacy to some extent and the objective perturbation always obtains higher classification performance than output perturbation when the privacy preserving degree is constant.