基于车辆协作的混淆路径轨迹隐私保护机制

doi:10.11959/j.issn.2096-109x.2019038

网络与信息安全学报 ›› 2019, Vol. 5 ›› Issue (4): 52-62.doi: 10.11959/j.issn.2096-109x.2019038

• 专栏：隐私保护关键技术及其新型应用模式探索 • 上一篇下一篇

基于车辆协作的混淆路径轨迹隐私保护机制

赵子文,叶阿勇(),金俊林,孟玲玉

福建师范大学数学与信息学院，福建福州 350007

修回日期:2019-03-18 出版日期:2019-08-15 发布日期:2019-08-20
作者简介:赵子文（1992- ）男，山东枣庄人，福建师范大学硕士生，主要研究方向为车联网数据安全与隐私保护。|叶阿勇（1977- ），男，福建漳州人，博士，福建师范大学教授，主要研究方向为信息隐私与安全。|金俊林（1994- ）男，江苏盐城人，福建师范大学硕士生，主要研究方向为手机端数据隐私保护。|孟玲玉（1994- ），女，黑龙江安达人，福建师范大学硕士生，主要研究方向为位置隐私保护。
基金资助:
国家自然科学基金资助项目(61771140);国家自然科学基金资助项目(6187208);国家自然科学基金资助项目(61872090);福建省自然科学基金资助项目(2018J01780)

Trajectory privacy protection mechanism of obfuscating paths based on vehicles cooperation

Ziwen ZHAO,Ayong YE(),Junlin JIN,Lingyu MENG

College of Mathematics and Informatics,Fujian Normal University,Fuzhou 350007,China

Revised:2019-03-18 Online:2019-08-15 Published:2019-08-20
Supported by:
The National Natural Science Foundation of China(61771140);The National Natural Science Foundation of China(6187208);The National Natural Science Foundation of China(61872090);The Natural Science Foundation of Fujian Province(2018J01780)

摘要/Abstract

摘要：

在车联网中，车辆通过和第三方共享位置信息获得基于位置的服务，这可能会导致车辆轨迹隐私泄露。针对该问题，提出基于车辆协作的混淆路径轨迹保护机制。首先，车辆轨迹熵达到自定义的轨迹保护阈值后，减少车辆混淆，解决了路径混淆开销大的问题。然后，设计路径混淆算法来增加车辆在路口路径混淆的机会，提高了车辆轨迹保护程度。最后，仿真实验从轨迹熵和轨迹跟踪成功率验证了该方法的有效性和高效性。

关键词: 车联网, 轨迹保护, 混淆路径, 轨迹熵, 基于位置的服务

Abstract:

Vehicles sent location information to third parties to obtain location-based services in the Internet of vehicles,which may lead to vehicle trajectory privacy leakage.To address the privacy problem,trajectory privacy protection mechanism of obfuscating paths based on vehicles cooperation was proposed.Firstly,after the vehicle trajectory entropy reaches the custom trajectory protection threshold,the overhead of paths confusion saves by reducing the number of vehicle paths confusion.Then,paths confusion algorithm was designed to increase chances of vehicle paths confusion at intersections,which can improve the degree of vehicle trajectory protection.Finally,the simulation experiment verified the validity and efficiency of the method from the trajectory entropy and trajectory tracking success rate.

Key words: Internet of vehicles, trajectory protection, obfuscating paths, trajectory entropy, location-based service

中图分类号:

TP393

赵子文,叶阿勇,金俊林,孟玲玉. 基于车辆协作的混淆路径轨迹隐私保护机制[J]. 网络与信息安全学报, 2019, 5(4): 52-62.

Ziwen ZHAO,Ayong YE,Junlin JIN,Lingyu MENG. Trajectory privacy protection mechanism of obfuscating paths based on vehicles cooperation[J]. Chinese Journal of Network and Information Security, 2019, 5(4): 52-62.

图/表 12

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

表1

参考文献 13

[1]	LIM J , YU H , KIM K ,et al. Preserving location privacy of connected vehicles with highly accurate location updates[J]. IEEE Communications Letters, 2017,21(3): 540-543.
[2]	BERESFORD A , STAJANO F . Location privacy in pervasive computing[J]. IEEE Pervasive Computing, 2003,2(1): 46-55.
[3]	FREUDIGER J , RAYA M , FELEGYHAZI M ,et al. Mix-zones for location privacy in vehicular networks[C]// Win-ITS 07. 2007.
[4]	PALANISAMYB , LIU L , . MobiMix:protecting location privacy with mix-zones over road networks[C]// IEEE 27th International Conference on Data Engineering(ICDE 2011). 2011: 494-505.
[5]	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.
[6]	FREUDIGER J , SHOKRI R , HUBAUX J P . On the optimal placement of mix zones[C]// The 9th International Symposium on Privacy Enhancing Technologies. Spring Berlin Heidelberg, 2009.
[7]	LIU X , ZHAO H , PAN M ,et al. Traffic-aware multiple mix zone placement for protecting location privacy[J]. Proceedings of the IEEE Infocom, 2012,131(5): 972-980.
[8]	NIU B , LI Q , ZHU X ,et al. Achieving k-anonymity in privacy-aware location-based services[C]// IEEE Infocom. 2014: 754-762.
[9]	NIU B , LI Q , ZHU X ,et al. Enhancing privacy through caching in location-based services[C]// IEEE Conference on Computer Communications. 2015: 1017-1025.
[10]	KIDO H , YANAGISAWA Y , SATOH T . Protection of location privacy using dummies for location-based services[C]// International Conference on Data Engineering. Tokyo:IEEE Computer Society, 2005:1248.
[11]	SUZUKI A , IWATA M , ARASE Y ,et al. A user location anonymization method for location based services in a real environment[C]// Sigspatial International Conference on Advances in Geographic Information Systems. 2010: 398-401.
[12]	KATO R , IWATA M , HARA T ,et al. A dummy-based anonymization 32method based on user trajectory with pauses[C]// International Conference on Advances in Geographic Information Systems. 2012: 249-258.
[13]	CUI J , WEN J , HAN S ,et al. Efficient privacy-preserving scheme for real-time location data in vehicular ad-hoc network[J]. IEEE Internet of Things Journal, 2018,5(10): 3491-3498.

方法	轨迹跟踪率	位置精度	盲区
OPVC	3%	高	无
Mix-zone	6.7%	高	有
Mix-group	3%	高	无
Silent period	10%	高	有

基于车辆协作的混淆路径轨迹隐私保护机制

Trajectory privacy protection mechanism of obfuscating paths based on vehicles cooperation

在线阅读

pdf下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 13

相关文章 8

Metrics

推荐阅读 0

[1]	陈赛特, 李卫海, 姚远志, 俞能海. 轻量级K匿名增量近邻查询位置隐私保护算法[J]. 网络与信息安全学报, 2023, 9(3): 60-72.
[2]	易杰, 曹腾飞, 郜帅, 黄建强. 车联网中基于攻防博弈的蜜罐防御及传输策略[J]. 网络与信息安全学报, 2022, 8(4): 157-167.
[3]	翟宝琴, 王健, 韩磊, 刘吉强, 何嘉豪, 刘天皓. 基于信任值的车联网分层共识优化协议[J]. 网络与信息安全学报, 2022, 8(3): 142-153.
[4]	徐堂炜,张海璐,刘楚环,肖亮,朱珍民. 基于强化学习的低时延车联网群密钥分配管理技术[J]. 网络与信息安全学报, 2020, 6(5): 119-125.
[5]	王瑞锦,唐榆程,张巍琦,张凤荔. 基于同态加密和区块链技术的车联网隐私保护方案[J]. 网络与信息安全学报, 2020, 6(1): 46-53.
[6]	周凯,彭长根,朱义杰,何建琼. 基于马尔可夫模型的同态加密位置隐私保护方案[J]. 网络与信息安全学报, 2017, 3(1): 54-60.
[7]	王良民,刘晓龙,李春晓,杨睛,杨卫东. 5G车联网展望[J]. 网络与信息安全学报, 2016, 2(6): 1-12.
[8]	王良民,李婷婷,陈龙. 基于车辆身份的车联网结构与安全[J]. 网络与信息安全学报, 2016, 2(2): 41-54.