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Privacy-Preserving Spatial Query Protocol Based on the Moore Curve for Location-Based Service
Computers & Security ( IF 4.8 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.cose.2020.101845
Huijuan Lian , Weidong Qiu , Di Yan , Jie Guo , Zhe Li , Peng Tang

Abstract The cloud computing has attracted a multitude of data owners to outsource computing and acquire location-based services from cloud service providers. The privacy-preserving spatial query on outsourced data has become a research hotspot in the location-based service. The existing schemes may damage the confidentiality of data, or be trapped by the large computation and communication overhead. The majority of them focus on the protection of original data and location information, while ignoring the significance of query records. In order to find a balance between security, efficiency and accuracy, this paper proposes a spatial transformation scheme for location-based services in outsourced environment, and designs a privacy-preserving k-nearest neighbor (k-NN) protocol. We utilize the Moore curve to perform a one-way transformation of the original data, and adopt the encryption to prevent malicious access to the transformed data by untrusted entities. The proposed protocol accomplishes efficient and accurate k-NN query in the transformed space while guaranteeing the confidentiality of outsourced data and the user's location. In addition, we propose a secure optimization method based on the oblivious transfer to protect the privacy of query records. Performance analysis shows that the proposed protocol has considerable efficiency advantage over existing schemes without sacrificing security and accuracy.

中文翻译:

基于Moore曲线的位置服务隐私保护空间查询协议

摘要 云计算吸引了大量数据所有者将计算外包并从云服务提供商处获取基于位置的服务。对外包数据的隐私保护空间查询已成为基于位置服务的研究热点。现有方案可能会破坏数据的机密性,或者被大量的计算和通信开销所困。他们中的大多数侧重于保护原始数据和位置信息,而忽略了查询记录的意义。为了在安全性、效率和准确性之间找到平衡,本文提出了一种外包环境下基于位置的服务的空间变换方案,并设计了一种隐私保护的k-最近邻(k-NN)协议。我们利用摩尔曲线对原始数据进行单向转换,并采用加密来防止不可信实体恶意访问转换后的数据。所提出的协议在保证外包数据和用户位置的机密性的同时,在转换后的空间中完成了高效准确的 k-NN 查询。此外,我们提出了一种基于不经意传输的安全优化方法来保护查询记录的隐私。性能分析表明,在不牺牲安全性和准确性的情况下,所提出的协议比现有方案具有相当大的效率优势。所提出的协议在保证外包数据和用户位置的机密性的同时,在转换后的空间中完成了高效准确的 k-NN 查询。此外,我们提出了一种基于不经意传输的安全优化方法来保护查询记录的隐私。性能分析表明,在不牺牲安全性和准确性的情况下,所提出的协议比现有方案具有相当大的效率优势。所提出的协议在保证外包数据和用户位置的机密性的同时,在转换后的空间中完成了高效准确的 k-NN 查询。此外,我们提出了一种基于不经意传输的安全优化方法来保护查询记录的隐私。性能分析表明,在不牺牲安全性和准确性的情况下,所提出的协议比现有方案具有相当大的效率优势。
更新日期:2020-09-01
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