Differentially private feature selection under MapReduce framework

Abstract

Privacy preserving data mining algorithms are crucial for the personal data analysis, such as medical and financial records. This paper focuses on feature selection and proposes a new privacy preserving distributed algorithm, which can effectively select features based on differential privacy and Gini index under the MapReduce framework. At the same time, the theoretic analysis for privacy guarantee is also presented. Some experiments are conducted on bench-mark datasets, the simulation results indicate that during the selection of important features, the proposed algorithm can preserve privacy information to a certain extent with less time cost than on centralized counterpart.