Participants in our research group

    本课题组参与人员：

尹凌、  许宁、  王倩、  汪伟、  宋晓晴

Keywords

    关键词:

mobile phone data; frequent activity locations; privacy protection; data mining

手机数据; 频繁活动点; 隐私保护; 数据挖掘

Description

    简介:

• In order to promote the legitimate use of large-scale individual activity data in urban planning and urban research, how to protect the frequent activity privacy of large-scale mobile phone call location data is studied. 为了促进大规模个体活动数据在城市规划与城市研究中的的合法使用，研究如何保护大规模手机通话位置数据的频繁活动隐私。

• Study how to protect the sensitive activity characteristics of large-scale moving trajectory data.研究如何保护大规模移动轨迹数据的敏感活动特征。
The large-scale mobile phone data has brought new opportunities to human activity research. However，recent studies found that the individual activity locations imbedded in mobile phone data easily lead to user re-identification，resulting in user privacy leak． Therefore，to avoid such privacy leak caused by frequent activity locations，this study first calculated the reidentification risk of a mobile user． Then it proposed a strategy of matching point sets to hide users with high re-identification risks． Finally，it used traffic flow analysis to evaluate the data utility loss caused by privacy protection procedures． Using the large-scale mobile phone data of Shenzhen city as an example，this study demonstrated that the proposed method can significantly reduce re-identification risks of mobile users and mitigate the data utility loss caused by privacy protection． This study can improve the privacy protection of large-scale mobile phone datasets，which can help promote safely using individual trajectory data of such kind and reasonably developing related laws and regulations. 大规模手机位置数据在为人类活动研究带来新的前景的同时，数据中蕴涵的个体活动点可能导致用户重识别，即隐私信息泄露。针对个体的频繁活动点集合，计算手机位置数据中的个体重识别风险，通过点集匹配策略实现高风险个体敏感位置信息的隐匿; 利用交通流分析来评价隐私保护后的数据可用性损失。基于深圳市大规模手机位置数据实验，结果表明提出的方法在降低整体隐私泄露风险和确保隐私保护后的数据可用性方面皆具有良好效果。研究成果对大规模手机位置数据隐私保护具有重要的促进作用，有助于保障此类大规模个体轨迹数据的安全应用以及相关法律法规的合理制定。

Publication

    论文:

Wei Wang, Ling Yin, 2017. Privacy protection method for mobile phone location data based on matching point sets of frequent activity locations. Application Research of Computers, 34(3), 867-870. 汪伟,尹凌.基于频繁活动点集的手机位置数据隐私保护方法[J/OL].计算机应用研究, 2017, 34(03): 867-870. (CSCD)

Ling Yin, Jinxing Hu, Qian Wang, Wei Wang, Zhiling Cai, 2016. Re-identification risk versus data utility for aggregated mobility research using mobile phone location data. Journal of Integration Technology, 5(2), 19-28. 尹凌,胡金星,王倩,汪伟,蔡芷铃. 大规模手机位置数据研究中的个体重识别风险及其与数据可用性的关系[J]. 集成技术, 2016, 5(02): 19-28.

Ling Yin, Qian Wang, Shih-Lung Shaw, Zhixiang Fang, Jinxing Hu, Ye Tao, Wei Wang, 2015. Re-identification risk versus data utility for aggregated mobility research using mobile phone location data. PloS one, 10(10), e0140589. (SCI)

	频繁活动点攻击模型下的隐私风险		随机点攻击模型下的隐私风险

Cooperations

    合作单位：

	Department of Geography, University of Tennessee, Knoxville, USA 美国田纳西大学			State Key Laboratory of Information Engineering in Surveying, Mapping, Remote and Sensing, Wuhan University,Wuhan 430079, China 武汉大学测绘遥感信息工程国家重点实验室			Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China 香港大学城市规划与设计系