Efficient and secure k -nearest neighbor query on outsourced data
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Efficient and secure k-nearest neighbor query on outsourced data Huijuan Lian 1 & Weidong Qiu 1 & Di Yan 2 & Zheng Huang 1 & Peng Tang 1 Received: 30 September 2019 / Accepted: 23 March 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract k-nearest neighbor (k-NN) query is widely applied to various networks, such as mobile Internet, peer-to-peer (P2P) network, urban road networks, and so on. The location-based service in the outsourced environment has become a research hotspot with the rise of cloud computing. Meanwhile, various privacy issues have been increasingly prominent. We propose an efficient privacy-preserving query protocol to accomplish the k-nearest neighbor (k-NN) query processing on outsourced data. We adopt the Moore curve to transform the spatial data into one-dimensional sequence and utilize the AES to encrypt the original data. According to the cryptographic transformation, the proposed protocol can minimize the communication overhead to achieve efficient k-NN query while protecting the spatial data and location privacy. Furthermore, the proposed efficient scheme offers considerable performance with privacy preservation. Experiments show that the proposed scheme achieves high accuracy and efficiency while preserving the data and location privacy when compared with the existing related approach. Keywords k-nearest neighbor . Privacy-preserving . Data outsourcing . Location-based service
1 Introduction With the rapid development of high-performance intelligent terminals, the location-based service (LBS) is booming prosperously in a variety of network systems, such as social network applications, P2P network, and so on. The explosively increasing spatial information brings massive data that exceeds the processing power of small and medium-sized companies and individuals. Data owners need to purchase a large amount of storage devices and employ professionals with expertise to manage and maintain the data. Emerging computing paradigms such as cloud computing and crowdsourcing have attracted many enterprises and organizations to consider utilizing the technological and financial benefits of the database service outsourcing. Cloud computing has brought profound influence to many research fields such as machine learning This article is part of the Topical Collection: Special Issue on Security and Privacy in Machine Learning Assisted P2P Networks Guest Editors: Hongwei Li, Rongxing Lu and Mohamed Mahmoud * Weidong Qiu [email protected] 1
School of Cyber Security, Shanghai Jiao Tong University, Shanghai 200240, China
2
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
[1], P2P network, searchable encryption [2] and so on. Cloud service provides flexible resource allocation and effectively reduces the management overhead of the data owner. Data owners can rent cloud computing platform resources according to the specific needs of different services. In this way, they can reduce the storage and computation costs while ensur
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