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Shinji Ono, Jun Takata, Masaharu Kataoka, Tomohiro I, Kilho Shin, Hiroshi Sakamoto

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Jun 30, 2022
Algorithms
DOI :
10.3390/a15070229
Article
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Algorithms, Vol. 15, Pages 229: Privacy Preserving Feature Selection with Fully Homomorphic Encryption Algorithms doi: 10.3390/a15070229 Authors: Shinji Ono Jun Takata Masaharu Kataoka Tomohiro I Kilho Shin Hiroshi Sakamoto For the feature selection problem, we propose an efficient privacy preserving algorithm. Let D, F, and C be data, feature, and class sets, respectively, where the feature value x(Fi) and the class label x(C) are given for each x∈D and Fi∈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(Fi)=y(Fi) for Fi∈F′, and minimal means that any proper subset of F′ is no longer consistent. On distributed datasets, we consider feature selection as a privacy preserving problem: assume that semi honest parties A and B have their own personal DA and DB. The goal is to solve the feature selection problem for DA∪DB without sacrificing their privacy. In this paper, we propose a secure and efficient algorithm based on fully homomorphic encryption, and we implement our algorithm to show its effectiveness for various practical data. The proposed algorithm is the first one that can directly simulate the CWC (Combination of Weakest Components) algorithm on ciphertext, which is one of the best performers for the feature selection problem on the plaintext.

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