Privacy-Preserving Multiparty Protocol for Feature Selection Problem
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In this paper, we propose a secure multiparty protocol for the feature selection problem. Let D be the set of data, F the set of features, and C the set of classes, where the feature value x(F_i) and the class x(C) are given for each x∈ D and F_i ∈ F. For a triple (D,F,C), the feature selection problem is to find a consistent and minimal subset F' ⊆ F, where `consistent' means that, for any x,y∈ D, x(C)=y(C) if x(F_i)=y(F_i) for F_i∈ F', and `minimal' means that any proper subset of F' is no longer consistent. The feature selection problem corresponds to finding a succinct description of D, and has various applications in the field of artificial intelligence. In this study, we extend this problem to privacy-preserving computation model for multiple users. We propose the first algorithm for the privacy-preserving feature selection problem based on the fully homomorphic encryption. When parties A and B possess their own personal data D_A and D_B, they jointly compute the feature selection problem for the entire data set D_A∪ D_B without revealing their privacy under the semi-honest model.
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