关联概率不可区分的位置隐私保护方法

doi:10.11959/j.issn.1000-436x.2017163

通信学报 ›› 2017, Vol. 38 ›› Issue (8): 37-49.doi: 10.11959/j.issn.1000-436x.2017163

关联概率不可区分的位置隐私保护方法

张磊^1,²,马春光¹(),杨松涛^1,²,李增鹏¹

¹ 哈尔滨工程大学计算机科学与技术学院，黑龙江哈尔滨 150001
² 佳木斯大学信息电子技术学院，黑龙江佳木斯 154007

修回日期:2017-06-14 出版日期:2017-08-01 发布日期:2017-09-07
作者简介:张磊（1982-），男，黑龙江绥化人，哈尔滨工程大学博士生，佳木斯大学讲师，主要研究方向为信息安全、隐私保护。|马春光（1974-），男，黑龙江双城人，博士，哈尔滨工程大学教授、博士生导师，主要研究方向为密码学、数据安全与隐私保护、无线自组织网络及安全。|杨松涛（1972-），男，黑龙江鹤岗人，博士，佳木斯大学教授，主要研究方向为信息安全、隐私保护。|李增鹏（1989-），男，山东青岛人，哈尔滨工程大学博士生，主要研究方向为密码学、密码协议。
基金资助:
国家自然科学基金资助项目(61472097);高等学校博士学科点专项科研基金资助项目(20132304110017);黑龙江省自然科学基金资助项目(F2015022)

Correlation probability indistinguishable location privacy protection algorithm

Lei ZHANG^1,²,Chun-guang MA¹(),Song-tao YANG^1,²,Zeng-peng LI¹

¹ College of Computer Science and Technology，Harbin Engineering University，Harbin 150001，China
² College of Information and Electronic Technology，Jiamusi University，Jiamusi 154007，China

Revised:2017-06-14 Online:2017-08-01 Published:2017-09-07
Supported by:
The Nationa1 Natura1 Science Foundation of China(61472097);The Specia1ized Research Fund for the Doctora1 Program of Higher Education(20132304110017);The Natura1 Science Foundation of Hei1ongjiang Province(F2015022)

摘要/Abstract

摘要：

首先量化了在快照查询服务和连续查询服务中攻击者可能通过关联关系建立的关联概率攻击方法。然后，针对这些攻击方法提出了与之对应的基于广义差分隐私的隐私保护模型。基于建立的隐私保护模型设计了基于位置偏移产生关联概率不可区分的隐私保护方法，并证明了这种方法的隐私保护效力。最后，通过实验进一步验证所提模型和方法的隐私保护效力和算法执行效率。

关键词: 基于位置服务, 关联概率, 差分隐私, 隐私保护

Abstract:

The attack of corre1ation probabi1ity used by the adversary in snapshot query and continuous query services were measured.Then，on account of these attacks，a privacy protection framework based on the differentia1 privacy and a 1ocation-shift scheme to achieve the indistinguishab1e of corre1ation probabi1ities was provided，and the protection effectiveness of this method was proved.At 1ast，security ana1ysis and the experiment resu1ts further verify the protection effectiveness and guarantee the execution efficiency of the proposed scheme.

Key words: 1ocation-based service, corre1ation probabi1ity, differentia1 privacy, privacy protection

中图分类号:

TP311

张磊,马春光,杨松涛,李增鹏. 关联概率不可区分的位置隐私保护方法[J]. 通信学报, 2017, 38(8): 37-49.

Lei ZHANG,Chun-guang MA,Song-tao YANG,Zeng-peng LI. Correlation probability indistinguishable location privacy protection algorithm[J]. Journal on Communications, 2017, 38(8): 37-49.

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参考文献 35

[1]	GRUTESER M , GRUNWALD D . Anonymous usage of location-based services through spatial and temporal cloaking[C]// 1st International Conference on Mobile Systems,Applications and Services. San Francisco,California,ACM, 2003: 31-42.
[2]	GEDIK B , LING L . Location privacy in mobile systems:a personalized anonymization model[C]// Distributed Computing Systems,ICDCS 2005. 2005: 620-629.
[3]	FUYU L , HUA K A , YING C . Query l-diversity in location-based services[C]// Mobile Data Management:Systems,Services and Middleware,2009 MDM′09 Tenth International Conference. 2009: 436-442.
[4]	REBOLLO-MONEDERO D , FORNE J , SOLANAS A ,et al. Private location-based information retrieval through user collaboration[J]. Computer Communications, 2010,33(6): 762-774.
[5]	REBOLLO-MONEDERO D , FORNE J , DOMINGO-FERRER J . Query profile obfuscation by means of optimal query exchange between users[J]. IEEE Transactions on Dependable and Secure Computing, 2012,9(5): 641-654.
[6]	SHOKRI R , THEODORAKOPOULOS G , PAPADIMITRATOS P ,et al. Hiding in the mobile crowd:location privacy through collaboration[J]. IEEE Transactions on Dependable and Secure Computing, 2014,11(3): 266-279.
[7]	NIU B , ZHU X Y , LI Q H ,et al. A novel attack to spatial cloaking schemes in location-based services[J]. Future Generation Computer Systems, 2015,49(C): 125-132.
[8]	KHOSHGOZARAN A , SHIRANI-MEHR H , SHAHABI C . SPIRAL:a scalable private information retrieval approach to location privacy[C]// 2008 Ninth International Conference on Mobile Data Management Workshops. 2008: 49-56.
[9]	KHOSHGOZARAN A , SHAHABI C , SHIRANI-MEHR H . Location privacy:going beyond K-anonymity,cloaking and anonymizers[J]. Knowledge and Information Systems, 2011,26(3): 435-465.
[10]	LIEN I T , LIN Y H , SHIEH J R ,et al. A novel privacy preserving location-based service protocol with secret circular shift for k-NN search[J]. IEEE Transactions on Information Forensics and Security, 2013,8(6): 863-873.
[11]	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, 2014,26(5): 1200-1210.
[12]	BEN N , QINGHUA L , XIAOYAN Z ,et al. Enhancing privacy through caching in location-based services[C]// Computer Communications(INFOCOM),2015 IEEE Conference. 2015: 1017-1025.
[13]	MA C G , ZHOU C L , YANG S T . A Voronoi-based location privacy-preserving method for continuous query in LBS[J]. International Journal of Distributed Sensor Networks, 2015,11(3): 1-17.
[14]	SCHLEGEL R , CHOW C Y , HUANG Q ,et al. User-defined privacy grid system for continuous location-based services[J]. IEEE Transactions on Mobile Computing, 2015,14(10): 2158-2172.
[15]	WANG Y , XIA Y , HOU J ,et al. A fast privacy-preserving framework for continuous location-based queries in road networks[J]. Journal of Network and Computer Applications, 2015,53: 57-73.
[16]	PALANISAMY B , LIU L . Attack-resilient mix-zones over road networks:architecture and algorithms[J]. IEEE Transactions on Mobile Computing, 2015,14(3): 495-508.
[17]	GAO S , MA J F , SHI W S ,et al. LTPPM:a location and trajectory privacy protection mechanism in participatory sensing[J]. Wireless Communications ＆ Mobile Computing, 2015,15(1): 155-169.
[18]	HWANG R H , HSUEH Y L , CHUNG H W . A novel time-obfuscated algorithm for trajectory privacy protection[J]. IEEE Transactions on Services Computing, 2014,7(2): 126-139.
[19]	OZER M , KELES I , TOROSL U ,et al. Predicting the next location change and time of change for mobile phone users[C]// Third ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems.Dallas,Texas,ACM. 2014: 51-59.
[20]	XUE A Y , ZHANG R , ZHENG Y ,et al. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction[C]// 29th IEEE International Conference on Data Engineering(ICDE).Brisbane,AUSTRALIA. 2013: 254-265.
[21]	SU H , ZHENG K , WANG H ,et al. Calibrating trajectory data for similarity-based analysis[C]// 2013 ACM SIGMOD International Conference on Management of Data. ACM, 2013: 833-844.
[22]	CHEN X , PANG J , XUE R . Constructing and comparing user mobility profiles[J]. ACM Transactions on the Web, 2014,8(4): 21-25.
[23]	LEI Z , CHUNGUANG M , SONGTAO Y . Location association similar based anonymus algorithm[J]. China Sciencepaper, 2016,11(2): 197-201,231.
[24]	NIU B , QINGHUA L , XIAOYAN Z ,et al. Achieving k-anonymity in privacy-aware location-based services[C]// INFOCOM,2014 Proceedings IEEE. 2014: 754-762.
[25]	DWORK C , . DIfferential privacy[C]// Lecture Notes in Computer Science. 2006: 1-12.
[26]	XIONG L . Adaptive differentially private data release for data sharing and data mining[J]. 2013 IEEE 13th International Conference on Data Mining Workshops(ICDMW), 2013: 891-891.
[27]	CHENG X , SU S , XU S Z ,et al. DP-Apriori:a differentially private frequent itemset mining algorithm based on transaction splitting[J]. Computers ＆ Security, 2015,50: 74-90.
[28]	XU J , ZHANG Z J , XIAO X K ,et al. Differentially private histogram publication[J]. VLDB Journal, 2013,22(6): 797-822.
[29]	RIBONI D , BETTINI C . Incremental release of differentially-private check-in data[J]. Pervasive and Mobile Computing, 2015,16: 220-238.
[30]	DEWRI R . Local differential perturbations:location privacy under approximate knowledge attackers[J]. IEEE Transactions on Mobile Computing, 2013,12(12): 2360-2372.
[31]	ANDRéS M E , BORDENABE N E , CHATZIKOKOLAKIS K ,et al. Geo-indistinguishability:differential privacy for location-based systems[C]// 2013 ACM SIGSAC Conference on Computer ＆ Communications Security. ACM, 2013: 901-914.
[32]	BORDENABE N E , CHATZIKOKOLAKIS K , PALAMIDESSI C . Optimal geo-indistinguishable mechanisms for location privacy[C]// 2014 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2014: 251-262.
[33]	PRIMAULT V , MOKHTAR S B , LAURADOUX C ,et al. Differentially private location privacy in practice[J]. arxiv:1410.7744.
[34]	CHATZIKOKOLAKIS K , PALAMIDESSI C , STRONATI M . Geo-indistinguishability:a principled approach to location privacy[M]// Distributed Computing and Internet Technology. 2015: 49-72.
[35]	PERAZZO P , DINI G . A uniformity-based approach to location privacy[J]. Computer Communications, 2015,64: 21-32.

关联概率不可区分的位置隐私保护方法

Correlation probability indistinguishable location privacy protection algorithm

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