基于频繁活动点集的手机位置数据隐私保护方法

摘要

The large-scale mobile phone data has brought new opportunities to human activity research.However,recent studies found that the individual activitylocations 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 ofmobile 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.