郭成

个人信息Personal Information

教授

博士生导师

硕士生导师

主要任职:软件学院、大连理工大学-立命馆大学国际信息与软件学院副院长

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:软件学院、国际信息与软件学院

学科:软件工程. 计算机应用技术

联系方式:guocheng@dlut.edu.cn

电子邮箱:guocheng@dlut.edu.cn

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Privacy preserving weighted similarity search scheme for encrypted data

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论文类型:期刊论文

第一作者:Guo, Cheng

通讯作者:Chang, CC (reprint author), Feng Chia Univ, Dept Informat Engn & Comp Sci, Taichung 40724, Taiwan.

合写作者:Tian, Pengxu,Chang, Chin-Chen

发表时间:2019-01-01

发表刊物:IET INFORMATION SECURITY

收录刊物:SCIE、Scopus

卷号:13

期号:1

页面范围:61-69

ISSN号:1751-8709

关键字:cryptography; outsourcing; cloud computing; data privacy; different weighted searches; encrypted data; searchable symmetric encryption; data users; search request; homomorphic encryption; weight problem; weight information; weighted similarity search scheme; cloud computing; outsourced data; privacy concerns; searchable encryption; different users; different requirements

摘要:Cloud computing has become increasingly popular among individuals and enterprises because of the benefits it provides by outsourcing their data to cloud servers. However, the security of the outsourced data has become a major concern. For privacy concerns, searchable encryption, which supports searching over encrypted data, has been proposed and developed rapidly in secure Boolean search and similarity search. However, different users may have different requirements on their queries, which mean different weighted searches. This problem can be solved perfectly in the plaintext domain, but hard to be addressed over encrypted data. In this study, the authors use locality-sensitive hashing (LSH) and searchable symmetric encryption (SSE) to deal with a privacy preserving weighted similarity search. In the authors' scheme, data users can generate a search request and set the weight for each attribute according to their requirements. They treat the LSH values as keywords and mix them into the framework of SSE. They use homomorphic encryption to securely address the weight problem and return the top-k data without revealing any weight information of data users. They formally analysed the security strength of their scheme. Extensive experiments on actual datasets showed that their scheme is extremely effective and efficient.