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基于位置服务中可更新的隐私保护k近邻查询

Updatable privacy-preserving -nearest neighbor query in location-based s-ervice.

作者信息

Wu Songyang, Xu Wenju, Hong Zhiyong, Duan Pu, Zhang Benyu, Hu Yupu, Wang Baocang

机构信息

The State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, 710071 China.

Facility of Intelligence Manufacture, Wuyi University, Jiangmen, 529020 China.

出版信息

Peer Peer Netw Appl. 2022;15(2):1076-1089. doi: 10.1007/s12083-021-01290-4. Epub 2022 Jan 7.

Abstract

The -nearest neighbor ( -NN) query is an important query in location-based service (LBS), which can query the nearest points to a given point, and provide some convenient services such as interest recommendations. Hence the privacy protection issue of -NN query has been a popular research area, protecting the information of queries and the queried results, especially in the information era. However, most of existing schemes fail to consider the privacy protection of location points already stored on servers. Or some schemes support no update of location points. In this paper, we present an updatable and privacy-preserving -NN query scheme to address the above two issues. Concretely, our scheme utilizes the D-tree ( -Dimensional tree) to store the location points of data owners in location service provider and encrypts the points with a distributed double-trapdoor public-key cryptosystem. Then, based on the Ciphertext Comparison Protocol and Ciphertext Euclidean Distance Calculation Protocol, our scheme can protect the privacy of location and query contents. Experimental analyses show our proposal supports some new location points for a fixed location service provider. Moreover, the queried results show a high accuracy of more than 95%.

摘要

k近邻(k-NN)查询是基于位置服务(LBS)中的一种重要查询,它可以查询到给定位置最近的k个点,并提供诸如兴趣推荐等便利服务。因此,k-NN查询的隐私保护问题一直是一个热门研究领域,特别是在信息时代,要保护查询信息和查询结果。然而,现有的大多数方案都没有考虑服务器上已存储的位置点的隐私保护。或者一些方案不支持位置点的更新。在本文中,我们提出了一种可更新且保护隐私的k-NN查询方案来解决上述两个问题。具体而言,我们的方案利用kd树(k维树)在位置服务提供商中存储数据所有者的位置点,并用分布式双陷门公钥密码系统对这些点进行加密。然后,基于密文比较协议和密文欧几里得距离计算协议,我们的方案可以保护位置和查询内容的隐私。实验分析表明,我们的方案支持为固定的位置服务提供商添加一些新的位置点。此外,查询结果显示准确率高达95%以上。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad38/8739704/60c6c8978d97/12083_2021_1290_Fig1_HTML.jpg

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