移动对象运动方式隐私保护

doi:10.3969/j.issn.1000-5641.2015.05.006

华东师范大学学报(自然科学版) ›› 2015, Vol. 2015 ›› Issue (5): 77-87.doi: 10.3969/j.issn.1000-5641.2015.05.006

移动对象运动方式隐私保护

许建秋，黄火荣

南京航空航天大学计算机科学与技术学院，南京210016

收稿日期:2015-07-09 出版日期:2015-09-25 发布日期:2015-10-08
通讯作者: 许建秋，男，副教授，硕士生导师，研究方向为移动对象数据库. E-mail: jianqiu@nuaa.edu.cn. E-mail:jianqiu@nuaa.edu.cn.
作者简介:许建秋，男，副教授，硕士生导师，研究方向为移动对象数据库. E-mail: jianqiu@nuaa.edu.cn.
基金资助:
国家自然科学基金青年基金(61300052)；江苏省自然科学基金青年基金(BK20130810)

Privacy preserving for moving objects with transportation modes

XU Jian-qiu,HUANG Huo-rong

Received:2015-07-09 Online:2015-09-25 Published:2015-10-08

摘要/Abstract

摘要： 现有的基于位置隐私保护研究主要提供对用户位置的保护，没有涉及运动方式，例如公交车、步行、驾车等.作为移动对象的重要属性之一，运动方式反映移动特点，对用户行为分析起到了重要的作用.针对包含多种运动方式的移动对象，提出了一种数据隐私保护方法.该方法针对范围查询，包含两种措施：①位置模糊；②查询重设，可有效地将精确运动方式隐藏，避免将真实数据返回给非法查询用户.对两种保护措施进行了分析比较，给出了如何将此方法融合到已有系统中.此外，在位置模糊和查询重设基础上，提出了不同保护粒度的措施使得数据保护度具有可调性以适应不同的应用需求.

关键词: 移动对象, 运动方式；隐私保护

Abstract: The current methods of locationbased privacy preserving focus on protecting the location of users, but do not consider transportation modes such as BUS, WALK and CAR. Being one key attribute of moving objects, transportation mode reflects the feature of mobile users and can be used to analyze their behavior. This paper proposes a method including cloaking location and reset modes to preserve transportation modes of moving objects for range queries. Such a technique prevents modes from being disclosed and avoids returning precise data to illegal users. We analyze the two methods and introduce how to integrate the solution into the existing system. In addition, cloaking location and reset modes with different preserving granularity is proposed to have a flexible and tunable method for different applications.

Key words: moving object, transportation mode, privacy preserving

中图分类号:

TP392

许建秋，黄火荣. 移动对象运动方式隐私保护[J]. 华东师范大学学报(自然科学版), 2015, 2015(5): 77-87.

XU Jian-qiu,HUANG Huo-rong. Privacy preserving for moving objects with transportation modes[J]. Journal of East China Normal University(Natural Sc, 2015, 2015(5): 77-87.

参考文献

［1］〖ZK(#〗ZHENG Y, LIU L, WANG L, et al. Learning transportation mode from raw GPS data for geographic applications on the web［C］Proceedings of the 17th International Conference on World Wide Web. New York: ACM, 2008: 247256.

［2］ZHENG Y, CHEN Y, XIE X, et al. Understanding transportation modes based on GPS data for Web applications ［J］. ACM Transactions on the Web, 2010, 4(1): 495507.

［3］REDDY S, MUN M, BURKE J, et al. Using mobile phones to determine transportation modes［J］. ACM Transactions on Sensor Networks (TOSN), 2010, 6(2): article 13.

［4］STENNETH L, WOLFSON O, YU P S, et al. Transportation mode detection using mobile phones and GIS information ［C］Proceedings of the 19th ACM SIGSPATIAC International Conference on Advances in Geographic Information Systems. New York: ACM, 2011: 5463.

［5］BETTINI C, WANG X S, JAJODIA S. Protecting privacy against locationbased personal identification［C］2nd VLDB Workshop Secure Data Management. Berlin: SpringerVerlag, 2005: 185199.

［6］薛娇,刘向宇，杨晓春，等. 一种面向公路网络的位置隐私保护方法［J］.计算机学报，2011，34（5）: 865878.

［7］霍峥,孟小峰.轨迹隐私保护技术研究［J］.计算机学报;2011,34(10):18201930.

［8］田秀霞,王晓玲,高明,等.数据库服务—安全与隐私保护［J］.软件学报,2010，21(5):9911006.

［9］CICEK A E, NERGIZ M E, SAYGIN Y. Ensuring location diversity in privacypreserving spatiotemporal data publishing［J］. the VLDB J, 2014, 23(4): 609625.

［10］XU J, GTING R H. A generic data model for moving objects［J］. Geoinformatica, 2013, 17(1): 125172.

［11］XU J, GTING R H, QIN X. et al. Benchmarking generic moving objects ［J］. Geoinformatica, 2015, 19(2): 227276.

［12］LIU L. From data privacy to location privacy: Models and algorithms［C］Proceedings of the 33rd International Conference on Very Large Data Bases. Seoul: VLDB Endowment.2007: 14291430.

［13］MOKBEL M F, CHOW C Y, AREF W G. The new casper: Query processing for location services without compromising privacy［C］Proceeding of the 32nd International Conference on Very Large Data Bases. Seoul: VLDB Endowment, 2006: 763774.

［14］SAMARATI P. Protecting respondent’s privacy in microdata release［J］. IEEE Trans. on Knowl. and Data Eng, 2011, 13(6): 10101027.

［15］GHINITA G, DAMIANI M L, BERTINO E, et al. Interactive location cloaking with the PROBE obfuscator［C］Mobile Data Management: System, Services and Middleware. Tenth International Conference on IEEE. ［s. l.］: IEEE, 2009: 355356.

［16］GKOULALASDIVANIS A, VERYKIOS V S. A Free Terrain Model for Trajectory KAnonymity［M］Database and Expert Systems Applications. New York: Springer, 2008: 4956.

［17］GRUTESER M, GRUNWALD D. Anonymous usage of locationbased services through spatial and temporalcloaking ［C］Proceedings of the 1st International Conference on Mobile Systems, Applications and Services. New York: ACM, 2003: 3142.

［18］KHOSHGOZARAN A, SHAHABI C, MEHR H S. Location privacy: Going beyond kanonymity, cloaking and anonymizers［J］. Knowl. and Inf. Syst, 2011, 26(3): 43546.

［19］GONG Z Q, SUN G Z, XIE X. Protecting privacy in locationbased services using kanonymity without cloaked region［C］11th International Conference on Mobile Data Management. USA: DBLP, 2010: 366371.

［20］YUNG D, LO E, YIU M L. Authentication of moving range queries［C］Proceedings of the 21st ACM International Conference on Information and Knowledge Management. New York: ACM, 2012: 13721381.

［21］XU J L, CHEN Q, HU H B. VERDICT: Privacypreserving authentication of range queries in locationbased services［C］Proceedings of the 2013 IEEE International Conference on Data Engineering. Washington, DC: IEEE, 2013: 13121315.

［22］HU H B, XU J L, CHEN Q, et al. Authenticating locationbased services without compromising location privacy ［C］ Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. New York: ACM, 2012: 301312.

［23］YAO B, LI F F, XIAO X K. Secure nearest neighbor revisited［M］29th International Conference on Data Engineering (ICDE).［s. l.］: IEEE, 2013: 733744.

［24］YI X, PAULET R, BERTINO E, et al. Practical k nearest neighbor queries with location privacy［M］30th International Conference on Data Engineering (ICDE)［s. l.］: IEEE, 2014: 640651.

［25］SHAO D X, JIANG K F, KISTER T, et al. Publishing trajectory with differential privacy: A priori vs. A posteriori sampling mechanisms［M］Database and Expert Systems Applications. New York: Springer, 2013: 357365.

［26］PELEKIS N, DIVANIS A G, VODAS M, et al. Privacyaware querying over sensitive trajectory data［C］Proceedings of the 20th ACM International Conference on Information and Knowledge Management. New York: ACM, 2011: 895904.

移动对象运动方式隐私保护

Privacy preserving for moving objects with transportation modes

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