支持区间查询的基于位置服务外包数据隐私保护方案

doi:10.3969/j.issn.1671-1122.2021.03.004

信息网络安全 ›› 2021, Vol. 21 ›› Issue (3): 26-36.doi: 10.3969/j.issn.1671-1122.2021.03.004

支持区间查询的基于位置服务外包数据隐私保护方案

周由胜¹^,²(), 王明¹, 刘媛妮²

1.重庆邮电大学计算机科学与技术学院,重庆 400065
2.重庆邮电大学网络空间安全与信息法学院,重庆 400065

收稿日期:2020-11-15 出版日期:2021-03-10 发布日期:2021-03-16
通讯作者: 周由胜 E-mail:zhouys@cqupt.edu.cn
作者简介:周由胜（1979—）,男,湖北,副教授,博士,主要研究方向为数据安全、认证与密钥协商|王明（1995—）,男,安徽,硕士研究生,主要研究方向为位置隐私保护|刘媛妮（1982—）,女,河南,副教授,博士,主要研究方向为网络安全、网络智能、未来网络、IP路由技术
基金资助:
国家自然科学基金(61702067);重庆市自然科学基金(cstc2020joymsxmX0343);重庆市留学人员回国创业创新支持计划(CX2018122)

Privacy Protection Scheme Supporting Interval Query for LBS Outsourced Data

ZHOU Yousheng¹^,²(), WANG Ming¹, LIU Yuanni²

1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. College of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2020-11-15 Online:2021-03-10 Published:2021-03-16
Contact: ZHOU Yousheng E-mail:zhouys@cqupt.edu.cn

摘要/Abstract

摘要：

随着云计算技术的迅猛发展,越来越多的LBS服务被外包到云上运行以减少本地的计算和存储成本。然而,外包环境下的云服务器通常被认为是一个半可信的实体,LBS提供商的数据安全和用户的个人隐私将会面临新的安全挑战。针对现有基于位置服务数据外包方案中不支持区间查询和隐私保护不足等问题,文章提出一种支持区间查询的LBS外包数据隐私保护方案,利用非对称向量积保值加密和公钥可搜索加密对LBS坐标和关键词进行加密,实现LBS数据的机密性和用户查询模式的隐私性;利用轻量级的矩阵运算使用户在不泄露查询区间的前提下准确获得所需LBS数据。在新用户注册方面,采用基于双线性配对运算实现用户身份认证。安全性和性能分析表明,文章方案较同类方案具有一定优势。

关键词: 隐私保护, 基于位置服务, 区间查询, 外包计算

Abstract:

With the rapid development of cloud computing technology, more and more LBS services are being outsourced to the cloud to reduce local computing and storage costs. However, cloud servers are generally considered to be a semi-trusted entity in outsourced environment, the data security of LBS providers and the personal privacy of users will face new security challenges. In view of the fact that the absence of interval query and sufficient privacy protection in the existing LBS data outsourcing using cloud computing, a LBS interval query scheme with privacy protection features is proposed. Asymmetric scalar-product-preserving encryption and public key searchable encryption are used to encrypt LBS coordinates and point interest keywords to realize the confidentiality of LBS data and the user query mode. Lightweight matrix computing enables users to accurately obtain LBS data without revealing query intervals. In terms of new user registration, the bilinear pairing operation structure is adopted to realize user identity authentication. The performance analysis demonstrates that the proposed scheme has some advantages over existing similar schemes.

Key words: privacy protection, location-based service, interval query, outsourcing computing

中图分类号:

TP309

周由胜, 王明, 刘媛妮. 支持区间查询的基于位置服务外包数据隐私保护方案[J]. 信息网络安全, 2021, 21(3): 26-36.

ZHOU Yousheng, WANG Ming, LIU Yuanni. Privacy Protection Scheme Supporting Interval Query for LBS Outsourced Data[J]. Netinfo Security, 2021, 21(3): 26-36.

图/表 8

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参考文献 26

[1]	PAULET R, KAOSAR M G, YI X, et al. Privacy-preserving and Content-protecting Location Based Queries[J]. IEEE Transactions on Knowledge and Data Engineering, 2013,26(5):1200-1210.
[2]	PENG Tao, LIU Qin, MENG Dacheng, et al. Collaborative Trajectory Privacy Preserving Scheme in Location-based Services[J]. Information Sciences, 2017,387(5):165-179.
[3]	SHAO Jun, LU Rongxing, LIN Xiaodong. FINE: A Fine-grained Privacy-preserving Location-based Service Framework for Mobile Devices [C]//IEEE. IEEE INFOCOM 2014-IEEE Conference on Computer Communications. April 27-May 2, 2014, Toronto, Canada. New York: IEEE, 2014: 244-252.
[4]	JIANG Hongbo, ZHAO Ping, WANG Chen. RobLoP: Towards Robust Privacy Preserving Against Location Dependent Attacks in Continuous LBS Queries[J]. IEEE/ACM Transactions on Networking, 2018,26(2):1018-1032.
[5]	BERESFORD A R, STAJANO F. Location Privacy in Pervasive Computing[J]. IEEE Pervasive Computing, 2003,2(1):46-55.
[6]	HWANG R H, HSUEH Y L, CHUNG H W. A Novel Time-obfuscated Algorithm for Trajectory Privacy Protection[J]. IEEE Transactions on Services Computing, 2013,7(2):126-139.
[7]	LI Lichun, LU Rongxing, HUANG Cheng. EPLQ: Efficient Privacy-preserving Location-based Query over Outsourced Encrypted Data[J]. IEEE Internet of Things Journal, 2015,3(2):206-218.
[8]	BERESFORD A R, STAJANO F. Location Privacy in Pervasive Computing[J]. IEEE Pervasive Computing, 2003 ( 1):46-55.
[9]	PALANISAMY B, LIU Ling. Attack-resilient Mix-zones over Road Networks: Architecture and Algorithms[J]. IEEE Transactions on Mobile Computing, 2014,14(3):495-508.
[10]	SUN Yipin, ZHANG Bofeng, ZHAO Baokang, et al. Mix-zones Optimal Deployment for Protecting Location Privacy in VANET[J]. Peer-to-Peer Networking and Applications, 2015,8(6):1108-1121.
[11]	KIDO H, YANAGISAWA Y, SATOH T. An Anonymous Communication Technique Using Dummies for Location-based Services [C]//ICPS ‘05. Proceedings. International Conference on Pervasive Services, July 11-14, 2005, Santorini, Greece. Piscataway: IEEE, 2005: 88-97.
[12]	YIU M L, JENSEN C S, HUANG Xuegang, et al. SpaceTwist: Managing the Trade-offs among Location Privacy, Query Performance, and Query Accuracy in Mobile Services [C]//IEEE. 2008 IEEE 24th International Conference on Data Engineering. April 7-12, 2008, Cancun, Mexico. Los Alamitos: IEEE, 2008: 366-375.
[13]	ZHAO Dapeng, JIN Yuanyuan, ZHANG Kai, et al. EPLA: Efficient Personal Location Anonymity[J]. GeoInformatica, 2018,22(1):29-47.
[14]	Vu, Khuong, ZHENG Rong, GAO Jie. Efficient Algorithms for K-anonymous Location Privacy in Participatory Sensing [C]//IEEE. 2012 Proceedings IEEE INFOCOM, March 25-30, 2012, Orlando, FL, USA. New York: IEEE, 2012: 2399-2407.
[15]	GEDIK B, LIU Ling. Protecting Location Privacy with Personalized K-anonymity: Architecture and Algorithms[J]. IEEE Transactions on Mobile Computing, 2007,7(1):1-18.
[16]	NIU Ben, LI Qinghua, ZHU Xiaoyan, et al. Achieving K-anonymity in Privacy-aware Location-based Services [C]//IEEE INFOCOM 2014-IEEE Conference on Computer Communications. April 27-May 2, 2014, Toronto, Canada. New York: IEEE, 2014: 754-762.
[17]	CHEN Jing, HE Kun, YUAN Quan, et al. Blind Filtering at Third Parties: An Efficient Privacy-preserving Framework for Location-based Services[J]. IEEE Transactions on Mobile Computing, 2018,17(11):2524-2535.
[18]	ZHU Hui, LIU Fen, LI Hui. Efficient and Privacy-preserving Polygons Spatial Query Framework for Location-based Services[J]. IEEE Internet of Things Journal, 2016,4(2):536-545. doi: 10.1109/JIOT.2016.2553083 URL
[19]	ZHANG Shaobo, WANG Guojun, BHUIYAN M Z A, et al. A Dual Privacy Preserving Scheme in Continuous Location-based Services[J]. IEEE Internet of Things Journal, 2018,5(5):4191-4200. doi: 10.1109/JIoT.6488907 URL
[20]	YANG Xue, YIN Fan, TANG Xiaohu. A Fine-grained and Privacy-preserving Query Scheme for Fog Computing-enhanced Location-based Service[EB/OL]. https://www.mdpi.com/1424-8220/17/7/1611, 2017-05-31.
[21]	ZHU Hui, LU Rongxing, HUANG Cheng, et al. An Efficient Privacy-preserving Location-based Services Query Scheme in Outsourced Cloud[J]. IEEE Transactions on Vehicular Technology, 2015,65(9):7729-7739. doi: 10.1109/TVT.2015.2499791 URL
[22]	ZENG Ming, ZHANG Kai, CHEN Jie, et al. P3GQ: A Practical Privacy-preserving Generic Location-based Services Query Scheme[J]. Pervasive and Mobile Computing, 2018,51:56-72.
[23]	KARRAS P, NIKITIN A, SAAD M, et al. Adaptive Indexing over Encrypted Numeric Data [C]//ACM. Proceedings of the 2016 International Conference on Management of Data, June 26-July 1, 2016, San Francisco California, USA. New York: ACM, 2016: 171-183.
[24]	XU Zhou, LIN Yaping, SANDOR V K A, et al. A Lightweight Privacy and Integrity Preserving Range Query Scheme for Mobile Cloud Computing[J]. Computers & Security, 2019,84(7):318-333.
[25]	WONG W K, CHEUNG D W, KAO B, et al. Secure KNN Computation on Encrypted Databases [C]//ACM. Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data. June 29-July 2, 2009, Providence Rhode Island USA. New York: ACM, 2009: 139-152.
[26]	OU LU, YIN Hui, QIN Zheng, et al. An Efficient and Privacy-preserving Multiuser Cloud-Based LBS Query Scheme[J]. Security & Communication Networks, 2018,2018(3):1-11.

符号	符号说明
$<>$	内积运算符
${{M}_{1}},{{M}_{2}}$	可逆矩阵
${{O}_{o}}$	$l-2$维向量
$b$	长度为$l$的比特串
$\{({{x}_{i}},{{y}_{i}}),{{W}_{i}},{{\alpha }_{i}}\}$	第$i$条POI数据
$\{({{x}_{u}},{{y}_{u}}),R\}$	用户坐标为$({{x}_{u}},{{y}_{u}})$且查询半径为R的圆形区域
$[{{\partial }_{1}},{{\partial }_{2}}]$	用户所查询的POI区间属性范围
$lo{{c}_{\partial }},lo{{c}_{-1}}$	POI中区间属性值I和数字-1在l维向量中的位置
$r,{{r}_{u}},\text{ }r\prime ,\alpha $	随机数
$\left\{ S{{K}_{L}},P{{K}_{L}} \right\}$	LSP公私钥对
$\left\{ S{{K}_{\text{S}}},P{{K}_{S}} \right\}$	云服务器公私钥对
$\left\{ S{{K}_{u}},P{{K}_{u}} \right\}$	用户公私钥对

符号	定义
$N$	LBS数据数量
${{T}_{e}}$	群上一次模幂运算时间开销
${{T}_{m}}$	群上一次模幂乘法运算时间开销
${{T}_{p}}$	双线性映射操作时间开销

阶段	LBS加密数据	陷门生成	数据检索
文献[20]方案	$2N{{T}_{e}}$	$2{{T}_{e}}$	$2N{{T}_{p}}$
文献[21]方案	$N\left( 6{{T}_{e}}+2{{T}_{m}} \right)$	$4{{T}_{e}}$	$lo{{g}_{N}}\left( 5{{T}_{m}}+2{{T}_{p}} \right)$
文献[26]方案	$N\left( {{T}_{p}}+{{T}_{e}} \right)$	$2{{T}_{e}}$	$2N({{T}_{p}}+{{T}_{m}})$
本文方案	$N\left( 2{{T}_{m}}+{{T}_{p}} \right)$	$3{{T}_{m}}$	$N\left( {{T}_{m}}+2{{T}_{p}} \right)$

	查询隐私	位置隐私	地理范围查询	关键词查询	区间属性查询	用户访问控制
文献[20]方案	√	√	√	√	×	×
文献[21]方案	√	√	√	×	×	×
文献[26]方案	√	√	√	√	×	√
本文方案	√	√	√	√	√	√

支持区间查询的基于位置服务外包数据隐私保护方案

Privacy Protection Scheme Supporting Interval Query for LBS Outsourced Data

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