基于位置混淆的轨迹隐私保护方法

doi:10.11959/j.issn.1000-436x.2018119

Abstract

Abstract:

In the process of continuous queries,a method of trajectory privacy protection based on location obfuscation was proposed to solve the problem that K-anonymity was difficult to guarantee user privacy in third party architectrue.Firstly,the (G-1) query obfuscation locations through the location prediction was obtained and the dummy location selection mechanism,and then sent them together with the user’s real query location to different anonymizers to form cloaking regions and sent them to the LBS server for queries,and the query results were returned to the user by different anonymizers.In this method,the user’s real query location was confused by the location obfuscation,and the attacker couldn’t deduce the user’s trajectory from a single anonymizer or the LBS server.The method can enhance the privacy of the user’s trajectory and can effectively solve the performance bottleneck in the single anonymizer structure.Security analysis shows the security of the proposed approach,and experiments show this method can reduce the number of interactions between the user and the LBS server and the overhead of the single anonymizer.

Key words: trajectory privacy, location obfuscation, location prediction, dummy location, anonymizer

CLC Number:

TP393

Shaobo ZHANG,Qin LIU,Guojun WANG. Trajectory privacy protection method based on location obfuscation[J]. Journal on Communications, 2018, 39(7): 81-91.

Figures/Tables 11

符号	描述
E、En	非对称和对称加密函数
k_i	用户生成的第i个随机密钥
Q	用户的查询内容
(x_i,y_i)	第i个查询位置坐标值
K	匿名度
G	用户每次查询包含的查询位置数目
g	用户轨迹上预测的查询位置数目
R	查询范围半径
CR _i	匿名域
Re _i	用户查询兴趣点的集合
$M S G_{A_{j}}^{U_{i}}$	用户第i个位置向第 j个匿名器发送的查询信息
$M S G_{S}^{A_{j}}$	第 j个匿名器转发查询信息给LBS服务器
$M S G_{A_{j}}^{S}$	LBS服务器返回结果给第 j个匿名器
$M S G_{U}^{A_{j}}$	第 j个匿名器转发查询结果给用户

References 21

[1]	YI X , PAULET R , BERTINO E ,et al. Practical approximate k nearest neighbor queries with location and query privacy[J]. IEEE Transactions on Knowledge and Data Engineering, 2016,28(6): 1546-1559.
[2]	PRIMAULT V , BOUTET A , MOKHTAR S B ,et al. Adaptive location privacy with ALP[C]// The 35th Symposium on Reliable Distributed Systems (SRDS). 2016: 269-278.
[3]	雷凯跃, 李兴华, 刘海 ,等. 轨迹发布中基于时空关联性的假轨迹隐私保护方案[J]. 通信学报, 2016,37(12): 156-164.
	LEI K Y , LI X H , LIU H ,et al. Dummy trajectory privacy protection scheme for trajectory publishing based on the spatiotemporal correlation[J]. Journal on Communications, 2016,37(12): 156-164.
[4]	GRISSA M , YAVUZ A , HAMDAOUI B . Preserving the location privacy of secondary users in cooperative spectrum sensing[J]. IEEE Transactions on Information Forensics ＆ Security, 2017,12(2): 418-431.
[5]	张学军, 桂小林, 伍忠东 . 位置服务隐私保护研究综述[J]. 软件学报, 2015,26(9): 2373-2395.
	ZHANG X J , GUI X L , WU Z D . Privacy preservation for location-based services:a survey[J]. Journal of Software, 2015,26(9): 2373-2395.
[6]	万盛, 李凤华, 牛犇 ,等. 位置隐私保护技术研究进展[J]. 通信学报, 2016,37(12): 1-18.
	WAN S , LI F H , NIU B ,et al. Research progress on location privacy-preserving techniques[J]. Journal on Communications, 2016,37(12): 1-18.
[7]	ARDAGNA C A , CREMONINI M , VIMERCATI S D C D ,et al. An obfuscation-based approach for protecting location privacy[J]. IEEE Transactions on Dependable ＆ Secure Computing, 2010,8(1): 13-27.
[8]	霍峥, 孟小峰, 黄毅 . PrivateChechIn:一种移动社交网络中的轨迹隐私保护方法与进展[J]. 计算机学报, 2013,36(4): 716-726.
	HUO Z , MENG X F , HUANG Y . PrivateChechIn:trajectory privacy-preserving for chech-in services in MSNS[J]. Chinese Journal of Computers, 2013,36(4): 716-726.
[9]	CHOW C Y , MOKBEL M F , LIU X . A peer-to-peer spatial cloaking algorithm for anonymous location-based service[C]// The 14th annual ACM International Symposium on Advances in Geographic Information Systems. 2006: 171-178.
[10]	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.
[11]	PENG T , LIU Q , MENG D ,et al. Collaborative trajectory privacy preserving scheme in location-based services[J]. Information Sciences, 2017,387: 165-179.
[12]	ZHANG Y , TONG W , ZHONG S . On designing satisfaction- ratio-aware truthful incentive mechanisms for k-anonymity location privacy[J]. IEEE Transactions on Information Forensics and Security, 2016,11(11): 2528-2541.
[13]	GEDIK B , LIU L . Protecting location privacy with personalized k-anonymous:architectrue and algorithms[J]. IEEE Transaction on Mobile Computing, 2008,7(1): 1-18.
[14]	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.
[15]	LIAO D , LI H , SUN G ,et al. Protecting user trajectory in location-based services[C]// IEEE Global Communications Conference (GLOBECOM). 2015: 1-6.
[16]	周长利, 马春光, 杨松涛 . 路网环境下保护 LBS 位置隐私的连续KNN查询方法[J]. 计算机研究与发展, 2015,52(11): 2628-2644.
	ZHOU C L , MA C G , YANG S T . Location privacy-preserving method for LBS continuous KNN query in road networks[J]. Journal of Computer Research and Development, 2015,52(11): 2628-2644.
[17]	PENG T , LIU Q , WANG G J . Enhanced location privacy preserving scheme in location-based services[J]. IEEE Systems Journal, 2017,11(1): 219-230.
[18]	GAO S , MA J F , SHI W S ,et al. TrPF:a trajectory privacy-preserving framework for participatory sensing[J]. IEEE Transactions on Information Forensics and Security, 2013,8(6): 874-887.
[19]	JEUNG H Y , SHEN H T , LIU Q ,et al. A hybrid prediction model for moving objects[C]// The 24th International Conference on Data Engineering. 2008: 70-79.
[20]	NIU B , ZHANG Z Y , LI X Q ,et al. Privacy-area aware dummy generation algorithms for location-based services[C]// The International Conference on Communications. 2014: 957-962.
[21]	BRINKHOFF T . Generating traffic data[J]. Bulletin of the Technical Committee Data Engineering, 2003,26(2): 19-25.

Metrics

Recommended 0

No Suggested Reading articles found!

参数	取值
用户数m/个	10 000
兴趣点数POI/个	10 000
匿名器数N/个	100
匿名度K/个	10~100
每次查询位置数G/个	10~100
查询半径R/km	0.5~1.5

连续查询次数	? =2	? =3	? =4	? =5	? =6	? =7	? =8	? =9	? =10
1	1	1	1	1	1	1	1	1	1
2	1	1	1	1	1	1	1	1	1
3	1	1	1	1	1	2	2	2	2
4	1	1	1	1	2	2	2	2	2
5	1	1	1	2	2	3	3	3	3
6	1	1	2	2	2	3	3	3	3
7	1	2	2	2	3	4	4	4	4
8	1	2	2	2	3	4	4	4	4
9	1	2	2	3	3	5	5	5	5
10	1	2	2	3	4	5	5	5	5

Trajectory privacy protection method based on location obfuscation

RichHTML

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 11

References 21

Related Articles 7

Metrics

Recommended 0

[1]	Si CHEN, Anmin FU, Mang SU, Huaijiang SUN. Trajectory privacy protection scheme based on differential privacy [J]. Journal on Communications, 2021, 42(9): 54-64.
[2]	Suxia ZHU, Shulun LIU, Guanglu SUN. Shape similarity differential privacy trajectory protection mechanism based on relative entropy and K-means [J]. Journal on Communications, 2021, 42(2): 113-123.
[3]	Ayong YE,Lingyu MENG,Ziwen ZHAO,Yiqing DIAO,Jiaomei ZHANG. Trajectory differential privacy protection mechanism based on prediction and sliding window [J]. Journal on Communications, 2020, 41(4): 123-133.
[4]	Jie WANG,Chunru WANG,Jianfeng MA,Hongtao LI. Dummy location selection algorithm based on location semantics and query probability [J]. Journal on Communications, 2020, 41(3): 53-61.
[5]	Feng-hua LI,Cui ZHANG,Ben NIU,Hui LI,Jia-feng HUA,Guo-zhen SHI. Efficient scheme for user's trajectory privacy [J]. Journal on Communications, 2015, 36(12): 114-123.
[6]	. Location prediction algorithm based on movement tendency [J]. Journal on Communications, 2014, 35(2): 7-53.
[7]	Wen LI,Shi-xiong XIA,Feng LIU,Lei ZHANG,Guan YUAN. Location prediction algorithm based on movement tendency [J]. Journal on Communications, 2014, 35(2): 46-53.