LBS中保护隐私的Skyline查询

Q1 Social Sciences HumanMachine Communication Journal Pub Date : 2010-04-24 DOI:10.1109/MVHI.2010.205
Zhefeng Qiao, Junzhong Gu, Xin Lin, Jing Chen
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引用次数: 9

摘要

Skyline查询被广泛应用于多标准决策、数据挖掘和可视化以及基于位置的服务(LBS)等应用中。以往关于天际线的研究主要集中在静态属性上,如Branch and Bound skyline和Probabilistic skyline。但是,由于LBS中保护个人位置和个人信息的隐私保护要求,需要关注的是用户的隐蔽区域,而不是用户的确切位置。为保护用户位置隐私,应在skyline中引入用户位置不确定等动态属性。本文提出了两种新的天际线查询方法——范围到范围的天际线查询(R2R)和点到范围的天际线查询(P2R)来解决LBS中的隐私问题。首先,针对数据空间属性都是动态的R2R天际线查询,提出了一种基于有效剪叶机制的R2RSQ算法;然后,利用R2RSQ算法的通用性,将其扩展到求解P2R天际线查询。最后,使用真实数据集的大量实验证明了我们提出的算法在回答R2R天际线查询方面的效率和有效性。实验结果表明,R2RSQ算法可以有效地支持隐私保护天际线,特别是在具有动态属性的大型数据集上,R2RSQ算法的有效性显著。
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Privacy-Preserving Skyline Queries in LBS
Skyline query is widely used in many applications, such as multi-criteria decision making, data mining and visualization, as well as Location-Based Services (LBS). The previous works about skyline mainly focuses on static attributes, such as Branch and Bound Skyline and Probabilistic Skyline. However, due to the requirements in the privacy-protection as protecting individual position and individual information in LBS, a cloaking region of a user instead of his exact position should be cared. To protect privacy of users’ location, dynamic attribute such as uncertain user position should be introduced to skyline. In this paper, two novel skylines query, Range to Ranges Skyline Query (R2R Skyline Query) and Point to Ranges Skyline Query (P2R Skyline Query), are introduced to deal with the privacy problems in LBS. Firstly we propose a R2RSQ algorithm, based on effectiveness pruning mechanism, to answer R2R skyline query, where the spatial attributes of data are all dynamic. Then, R2RSQ algorithm is extended to solve P2R skyline query by its generality. Lastly, extensive experiments using real data sets demonstrate the efficiency and effectiveness of our proposed algorithms in answering R2R skyline query. Our experimental results show that Privacy-Preserving skylines are significant and useful, and R2RSQ algorithm can efficiently support Privacy-Preserving skylines, especially, R2RSQ is efficient on large datasets with dynamic attributes.
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来源期刊
CiteScore
10.00
自引率
0.00%
发文量
10
审稿时长
8 weeks
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