Year: 2022
Type: article
Abstract: 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.
Source: Algorithms
DOI: 10.3390/a15070229
Authors: Shinji Ono, Jun Takata, Masaharu Kataoka, Tomohiro I, Kilho Shin, Hiroshi Sakamoto
Institutions: Kyushu Institute of Technology, Gakushuin University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,466
OpenAlex ID: W4283765049