《计算机应用研究》|Application Research of Computers

群智感知网络个性化位置隐私保护算法

Personalized location privacy protection algorithm in crowd sensing networksHu Min, Zhang Yan, Huang Hongcheng(School of Communication & Information Engineering, Chongqing University of Posts & Telecommunications, Chongqing 400065, China )

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作者 胡敏,张艳,黄宏程
机构 重庆邮电大学 通信与信息工程学院,重庆 400065
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文章编号 1001-3695(2019)03-058-0930-05
DOI 10.19734/j.issn.1001-3695.2017.10.1018
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。
关键词 位置隐私保护;个性化;敏感等级;群智感知网络
基金项目 重庆市科委基础与前沿研究项目(cstc2014jcyjA40039)
本文URL http://www.arocmag.com/article/01-2019-03-058.html
英文标题 Personalized location privacy protection algorithm in crowd sensing networksHu Min, Zhang Yan, Huang Hongcheng(School of Communication & Information Engineering, Chongqing University of Posts & Telecommunications, Chongqing 400065, China )
作者英文名 Hu Min, Zhang Yan, Huang Hongcheng
机构英文名 SchoolofCommunication&InformationEngineering,ChongqingUniversityofPosts&Telecommunications,Chongqing400065,China
英文摘要 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.
英文关键词 location privacy protection; personalization; sensitive level; crowd sensing networks
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收稿日期 2017/10/31
修回日期 2017/12/15
页码 930-934
中图分类号 TP309.2
文献标志码 A