群智感知网络个性化位置隐私保护算法
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摘要
群智感知网络中现有隐私保护算法对所有位置采用相同的隐私保护策略,导致位置隐私或保护过度或保护不足,且获得的感知数据精度较低。针对这一问题,提出了一种满足用户个性化隐私安全需求的位置隐私保护算法。首先,根据用户的历史移动轨迹,挖掘用户对不同位置的访问时长、访问频率以及访问的规律性来预测位置对用户的社会属性;然后,结合位置的自然属性,预测用户—位置的敏感等级;最后,结合用户在不同的位置有不同的隐私安全需求的特点,设置动态的隐私判定方案,在每个位置选择敏感度低的用户参与感知任务,以确保用户在隐私安全的前提下,贡献时空相关性精确高的感知数据。仿真结果表明,该算法在提高隐私保护水平的同时还提高了感知数据的精度。
The existing privacy protection strategies in crowd sensing networks used the same privacy policies for all locations which overprotected led to the problems that some locations,others were not adequately protected and the sensing data was less accurate. In order to solve this problem,this paper proposed a location privacy protection algorithm to meet the users' personalized privacy and security requirements. First,it mined users' access duration,frequency and regularity at different locations according to the user's historical movement trajectory,which used to predict the social attributes of the locations to the users.Then,it combined the location's social attributes and natural attributes to predict user-location sensitivity levels. Finally,considering the different privacy security requirements of users in different locations,it set a dynamic privacy decision scheme.It selected users with less sensitivity at each location to participate in sensing tasks to ensure that users,in the safe privacy context,could contribute the accurate data with a higher level of spatiotemporal correlation. The simulation results show that the algorithm can improve the privacy protection level and the accuracy of the sensing data.
The existing privacy protection strategies in crowd sensing networks used the same privacy policies for all locations which overprotected led to the problems that some locations,others were not adequately protected and the sensing data was less accurate. In order to solve this problem,this paper proposed a location privacy protection algorithm to meet the users' personalized privacy and security requirements. First,it mined users' access duration,frequency and regularity at different locations according to the user's historical movement trajectory,which used to predict the social attributes of the locations to the users.Then,it combined the location's social attributes and natural attributes to predict user-location sensitivity levels. Finally,considering the different privacy security requirements of users in different locations,it set a dynamic privacy decision scheme.It selected users with less sensitivity at each location to participate in sensing tasks to ensure that users,in the safe privacy context,could contribute the accurate data with a higher level of spatiotemporal correlation. The simulation results show that the algorithm can improve the privacy protection level and the accuracy of the sensing data.
引文
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[2] Sun Wen,Liu Jiajia. Congestion-aware communication paradigm forsustainable dense mobile crowd-sensing[J]. IEEE Communica-tions Magazine,2017,55(3):62-67.
[3]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 Xuejun,Gui Xiaolin,Wu Zhongdong. Review of research on location service privacy protec-tion[J]. Journal of Software,2015,26(9):2373-2395.)
[4] Shen Hang,Bai Guangwei,Yang Mei,et al. Protecting trajectoryprivacy:a user-centric analysis[J]. Journal of Network&Com-puter Applications,2017,82(3):128-139.
[5] Mehta K,Liu D,Wright M. Protecting location privacy in sensor net-works against a global eavesdropper[J]. IEEE Trans on MobileComputing,2011,11(2):320-336.
[6] Yang Dejun,Fang Xi,Xue Guoliang. Truthful incentive mechanismsfor k-anonymity location privacy[C]//Proc of IEEE INFOCOM. Pis-cataway,NJ:IEEE Press,2013:2994-3002.
[7] Ziegeldorf J H,Henze M,Bavendiek J,et al. Trace Mixer:privacy-preserving crowd-sensing sans trusted third party[C]//Proc of the13th Annual Conference on Wireless On-demand Network Systems andServices. Piscataway,NJ:IEEE Press,2017:17-24.
[8] Yu Zhiting,Qian Quan,Lin Chunyuan,et al. High performance data-fly based anonymity algorithm and its l-diversity[J]. InternationalJournal of Grid and High Performance Computing,2015,7(3):85-100.
[9]张伊璇,何泾沙,赵斌,等.一个基于博弈理论的隐私保护模型[J].计算机学报,2016,39(3):615-627.(Zhang Yixuan,HeJingsha,Zhao Bin,et al. A privacy protection model based on gametheory[J]. Journal of Computer,2016,39(3):615-627.)
[10] Zhang Ning,Li Ming,Lou Wenjing. Distributed data mining withdifferential privacy[C]//Proc of IEEE International Conference onCommunications. Piscataway,NJ:IEEE Press,2011:1-5.
[11]Li Meng,Zhu Liehuang,Zhang Zijian,et al. Achieving differentialprivacy of trajectory data publishing in participatory sensing[J]. In-formation Sciences,2017(8):1-13.
[12]Wei Ziling,Zhao Baokang,Liu Yujing,et al. PPSense:a novel pri-vacy-preserving system in people-centric sensing networks[C]//Procof the 8th International ICST Conference on Communications and Net-working in China(CHINACOM). Piscataway,NJ:IEEE Press,2013:461-467.
[13] Liu Bozhong,Chen Ling,Zhu Xingquan,et al. Protecting locationprivacy in spatial crowd sourcing using encrypted data[C]//Proc ofthe 20th International Conference on Extending Database Technology.Venice,Italy:OpenProceedings,2017:21-24.
[14]Krumm J. A survey of computational location privacy[J]. Personaland Ubiquitous Computing,2009,13(6):391-399.
[15]Agir B,Papaioannou T G,Narendula R,et al. User-side adaptiveprotection of location privacy in participatory sensing[J]. Geoinfor-matica,2014,18(1):165-191.
[16] Henderson T,Kotz D,Abyzov I. The changing usage of a maturecampus-wide wireless network[J]. Computer Networks,2008,52(14):2690-2712.
[17]Prabhala B,Porta T L. Spatial and temporal considerations in nextplace predictions[C]//Proc of IEEE Conference on Computer Com-munications Workshops(INFOCOM WKSHPS). Piscataway,NJ:IEEE Press,2015:390-395.
[18]Zheng Yu,Xie Xing,Ma Weiying. Geo Life:a collaborative socialnetworking service among user,location and trajectory[J]. Bulletinof the Technical Committee on Data Engineering,2010,33(2):32-39.