移动群智感知中用户隐私度量与隐私保护研究

doi:10.3969/j.issn.1671-1122.2018.08.009

摘要/Abstract

摘要：

针对移动群智感知中,现有隐私保护方案因缺乏有效的隐私度量手段而对所有感知数据采用统一的隐私保护策略,导致对感知数据的隐私信息保护过度或保护不足,且获得的感知数据的准确度较低的问题,文章提出一种面向移动群智感知的用户隐私度量方法,并基于该方法构建一种个性化隐私保护方案。首先,根据感知用户的历史时空数据,利用模糊推理技术,结合该位置所具有的公众属性和该位置对某一用户所具有的不同的个性属性等度量指标进行隐私度量,得出用户在不同位置的隐私度;然后感知平台根据用户上传的不同隐私度,在每个位置选择隐私度较低的用户参与感知任务,以确保用户在隐私安全的前提下,贡献准确度高的感知数据。仿真结果表明,文章方案在提高隐私保护水平的同时确保了感知数据的准确度。

关键词: 移动群智感知, 隐私度量, 时空数据, 隐私保护

Abstract:

In view of mobile crowdsensing, the existing privacy protection schemes use an unified privacy protection strategy for all perceived data because of the lack of effective privacy measurement methods, which leads to the problem of excessive protection or insufficient protection of the privacy information of the perceived data, and the accuracy of the perceived data is low, this paper proposes an user privacy measurement method for mobile crowdsensing and constructs a personalized privacy protection scheme based on this method. Firstly, according to the historical and spatio-temporal data of the sensing users, by using the fuzzy reasoning technology, combining the public attribute of the location and the different personality attributes of the location to a user, the privacy measurements of the user in different locations are obtained. Furthermore, the sensing platform selects a sensing user with low privacy measurement to participate in the sensing task in each location according to the different privacy measurement that user uploads, which ensures that users can contribute to the perceived data with high accuracy under the premise of privacy security. The simulation results show that the scheme ensures the accuracy of the perceived data while improves the level of privacy protection.

Key words: mobile crowdsensing, privacy measurement, spatio-temporal data, privacy protection

中图分类号:

TP309

马蓉, 陈秀华, 刘慧, 熊金波. 移动群智感知中用户隐私度量与隐私保护研究[J]. 信息网络安全, 2018, 18(8): 64-72.

Rong MA, Xiuhua CHEN, Hui LIU, Jinbo XIONG. Research on User Privacy Measurement and Privacy Protection in Mobile Crowdsensing[J]. Netinfo Security, 2018, 18(8): 64-72.

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参考文献 20

[1]	GUO Bin, YU Zhiwen, ZHOU Xingshe, et al.From Participatory Sensing to Mobile Crowd Sensing[C]//IEEE. 2014 IEEE International Conference on Pervasive Computing and Communications Workshops, March 24-28, 2014, Budapest, Hungary.NJ:IEEE, 2014: 593-598.
[2]	YU Ruiyun, WANG Pengfei, BAI Zhihong, et al.Participatory Sensing: People-Centric Smart Sensing and Computing[J]. Journal of Computer Research and Development, 2017, 54(3): 457-473.
	于瑞云, 王鹏飞, 白志宏,等. 参与式感知:以人为中心的智能感知与计算[J]. 计算机研究与发展, 2017, 54(3): 457-473.
[3]	ZHANG Jiafan, GUO Bin, LU Xinjiang, et al.Approach for Urban Popular Event Detection Using Mobile Crowdsourced Data[J]. Computer Science, 2015,42(S1): 5-9,37.
	张佳凡,郭斌,路新江,等.基于移动群智数据的城市热点事件感知方法[J].计算机科学,2015,42(S1):5-9,37.
[4]	KIM S, Robson C, Zimmerman T, et al.Creek Watch: Pairing Usefulness and Usability for Successful Citizen Science[C] //ACM. The SIGCHI Conference on Human Factors in Computing Systems, May 7-12, 2011,Vancouver, BC, Canada. New York: ACM, 2011:2125-2134.
[5]	POURNAJAF L, GARCIA-ULLOA D A, XIONG Li, et al. Participant Privacy in Mobile Crowd Sensing Task Management: A Survey of Methods and Challenges[J]. Acm Sigmod Record, 2015, 44(4): 23-34.
[6]	MA Rong, XIONG Jinbo, LIN Mingwei, et al.Privacy Protection-oriented Mobile Crowdsensing Analysis Based on Game Theory[C]//IEEE. The 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, August 1-4, 2017, Sydney, NSW, Australia. NJ:IEEE, 2017:990-995.
[7]	BAO Guohua, WANG Shengyu, LI Yunfa.Research on Data Security Protection Method Based on Privacy Awareness in Cloud Computing[J].Netinfo Security,2017,17(1):84-89.
	包国华, 王生玉, 李运发. 云计算中基于隐私感知的数据安全保护方法研究[J]. 信息网络安全, 2017,17(1):84-89.
[8]	YANG Dejun, FANG Xi, XUE Guoliang.Truthful Incentive Mechanisms for k-anonymity Location Privacy[C]// IEEE. 2013IEEE INFOCOM, April 14-19, 2013, Turin, Italy. NJ: IEEE, 2013: 2994-3002.
[9]	ZIEGELDORF J H, HENZE M, BAVENDIEK J, et al.TraceMixer: Privacy-preserving Crowd-sensing Sans Trusted Third Party[C]//IEEE. 2017 13th Annual Conference on Wireless On-demand Network Systems and Services (WONS), February 21-24, 2017, Jackson, WY, USA.NJ: IEEE, 2017: 17-24.
[10]	YU Zhiting, QIAN Quan, LIN Chunyuan, et al.High Performance Datafly-based Anonymity Algorithm and Its L-diversity[J]. International Journal of Grid and High Performance Computing, 2015, 7(3): 85-100.
[11]	ZHANG Ning, LI Ming, LOU Wenjing.Distributed Data Mining with Differential Privacy[C]// IEEE. 2011 IEEE International Conference on Communications, June 5-9, 2011, Kyoto, Japan .NJ: IEEE, 2011: 1-5.
[12]	WEI Ziling, ZHAO Baokang, LIU Yujing, et al.PPSense: a Novel Privacy-preserving System in People-centric Sensing Networks[C]// IEEE. 2013 8th International Conference on Communications and Networking in China, August 14-16, 2013, Guilin, China.NJ: IEEE,2013: 461-467.
[13]	LIU Bozhong, CHEN Ling, ZHU Xingquan, et al. Protecting Location Privacy in Spatial Crowdsourcing Using Encrypted Data [EB/OL]. , 2018-5-25.
[14]	WU Shasha, XIONG Jinbo, YE Guohua, et al.Research on Location Privacy Protection Based on Dummy Locations in Mobile Internet Environment[J].Netinfo Security,2016,16(10):54-59.
	吴莎莎,熊金波,叶帼华,等,移动互联网环境下基于假位置的位置隐私保护研究[J].信息网络安全,2016,16(10):54-59.
[15]	KRUMM J.A Survey of Computational Location Privacy[J]. Personal and Ubiquitous Computing, 2009, 13(6): 391-399.
[16]	AGIR B, PAPAIOANNOU T G, NARENDULA R, et al.User-side Adaptive Protection of Location Privacy in Participatory Sensing[J]. Geoinformatica, 2014, 18(1): 165-191.
[17]	HENDERSON T, KOTZ D, ABYZOV I.The Changing Usage of a Mature Campuswide Wireless Network[J]. Computer Networks, 2008, 52(14): 2690-2712.
[18]	DONG Xiaoning, ZHAO Huarong, LI Dianwei, et al.Research on Information Systems Security Risk Assessment Based on Fuzzy Theory of Evidence[J]. Netinfo Security,2017,17(5):69-73.
	董晓宁, 赵华容, 李殿伟,等. 基于模糊证据理论的信息系统安全风险评估研究[J]. 信息网络安全, 2017, 17(5):69-73.
[19]	NAN Wenqian, GUO Bin, CHEN Huihui, et al.A Cross-space, Multi-interaction-based Dynamic Incentive Mechanism for Mobile Crowdsensing[J]. Chinese Journal of Computers, 2015,38(12):2412-2425.
	南文倩, 郭斌, 陈荟慧,等, 基于跨空间多元交互的群智感知动态激励模型[J]. 计算机学报, 2015, 38(12):2412-2425.
[20]	ZHENG Yu, XIE Xing, MA Weiying.GeoLife: a Collaborative Social Networking Service among User, Location and Trajectory[J]. Bulletin of the Technical Committee on Data Engineering, 2010, 33(2): 32-39.