A Study on a DDH-Based Keyed Homomorphic Encryption Suitable to Machine Learning in the Cloud

被引：0

作者：

Tsuruta, Takuya ^{[1
]}

Araki, Shunsuke ^{[1
]}

Miyazaki, Takeru ^{[2
]}

Uehara, Satoshi ^{[3
]}

Kakizaki, Ken'ichi ^{[1
]}

机构：

[1] Kyushu Inst Technol, Fukuoka, Japan

[2] Kyushu Inst Informat Sci, Fukuoka, Japan

[3] Univ Kitakyushu, Fukuoka, Japan

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN, IEEE ICCE-TW 2022 | 2022年

关键词：

keyed homomorphic public key encryption; machine learning;

D O I：

10.1109/ICCE-TAIWAN55306.2022.9869098

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Homomorphic encryption is suitable for a machine learning in the cloud such as a privacy-preserving machine learning. However, ordinary homomorphic public key encryption has a problem that public key holders can generate ciphertexts and anyone can execute homomorphic operations. In this paper, we will propose a solution based on the Keyed Homomorphic-Public Key Encryption proposed by Emura et al.

引用

页码：167 / 168

页数：2

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