Visible to the public Efficient and Secure k-Nearest Neighbor Search Over Encrypted Data in Public Cloud

TitleEfficient and Secure k-Nearest Neighbor Search Over Encrypted Data in Public Cloud
Publication TypeConference Paper
Year of Publication2019
AuthorsSong, Fuyuan, Qin, Zheng, Liu, Qin, Liang, Jinwen, Ou, Lu
Conference NameICC 2019 - 2019 IEEE International Conference on Communications (ICC)
Date Publishedmay
Keywordsauthorisation, authorized data users, cloud computing, cryptography, data owners, data points, data privacy, data sharing, data storage, Electric fields, Electron traps, encrypted data, Impact ionization, Junctions, k-nearest neighbor search, k-NN search schemes, Mathematical model, matrix algebra, matrix similarity, Measurement, Metrics, nearest neighbor search, nearest neighbour methods, Photoconductivity, pubcrawl, public cloud, query privacy, search problems, search services, secure query services, security analysis, security property, valuable outsourced data
AbstractCloud computing has become an important and popular infrastructure for data storage and sharing. Typically, data owners outsource their massive data to a public cloud that will provide search services to authorized data users. With privacy concerns, the valuable outsourced data cannot be exposed directly, and should be encrypted before outsourcing to the public cloud. In this paper, we focus on k-Nearest Neighbor (k-NN) search over encrypted data. We propose efficient and secure k-NN search schemes based on matrix similarity to achieve efficient and secure query services in public cloud. In our basic scheme, we construct the traces of two diagonal multiplication matrices to denote the Euclidean distance of two data points, and perform secure k-NN search by comparing traces of corresponding similar matrices. In our enhanced scheme, we strengthen the security property by decomposing matrices based on our basic scheme. Security analysis shows that our schemes protect the data privacy and query privacy under attacking with different levels of background knowledge. Experimental evaluations show that both schemes are efficient in terms of computation complexity as well as computational cost.
Citation Keysong_efficient_2019