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Privacy-Preserving Feature Selection with Fully Homomorphic Encryption

Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi 820-8502, Japan
Computer Centre, Gakushuin University, 1-5-1 Mejiro, Toshimaku, Tokyo 171-8588, Japan
Author to whom correspondence should be addressed.
Academic Editor: Quan Qian
Algorithms 2022, 15(7), 229;
Received: 30 May 2022 / Revised: 21 June 2022 / Accepted: 26 June 2022 / Published: 30 June 2022
(This article belongs to the Special Issue Privacy Preserving Machine Learning)
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 xD and FiF. For a triple (D,F,C), the feature selection problem is to find a consistent and minimal subset FF, where ‘consistent’ means that, for any x,yD, x(C)=y(C) if x(Fi)=y(Fi) for FiF, 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 DADB 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. View Full-Text
Keywords: CWC algorithm; oblivious sorting; TFHE; IND-CPA CWC algorithm; oblivious sorting; TFHE; IND-CPA
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MDPI and ACS Style

Ono, S.; Takata, J.; Kataoka, M.; I, T.; Shin, K.; Sakamoto, H. Privacy-Preserving Feature Selection with Fully Homomorphic Encryption. Algorithms 2022, 15, 229.

AMA Style

Ono S, Takata J, Kataoka M, I T, Shin K, Sakamoto H. Privacy-Preserving Feature Selection with Fully Homomorphic Encryption. Algorithms. 2022; 15(7):229.

Chicago/Turabian Style

Ono, Shinji, Jun Takata, Masaharu Kataoka, Tomohiro I, Kilho Shin, and Hiroshi Sakamoto. 2022. "Privacy-Preserving Feature Selection with Fully Homomorphic Encryption" Algorithms 15, no. 7: 229.

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