Hierarchical differential privacy hybrid decomposition algorithm for location big data
Yan, Yan1,2; Hao, Xiaohong1; Zhang, Lianxiu2
2019-07
发表期刊CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
ISSN1386-7857
卷号22页码:S9269-S9280
摘要The biggest feature of the era of big data is that people can easily generate, access, and make use of massive data resources. As one of the most important and popular kind of big data, location big data and its application technology provide users with convenient services. However, improper collection, analysis and publishing of location big data also brings huge crisis of personal privacy disclosure. Spatial decomposition is one of the effective ways to achieve the statistics publication of location big data. In order to make full use of the redundant characteristics of location big data in spatial and temporal distribution, a hierarchical differential privacy hybrid decomposition algorithm is proposed in this paper. In the first layer of decomposition, an adaptive density grid structure is used to cluster the location big data, which not only reduces the uniform assumption errors but also avoids noise errors caused by large number of empty nodes. In order to guide the reasonable decomposition for skewed grids in the second layer, a heuristic quad-tree decomposition algorithm based on regional uniformity is designed, which solved the difficult problem for determining stop condition of the top-down decomposition of two-dimensional space. Comparative experiments show that the hierarchical differential privacy hybrid decomposition algorithm proposed in this paper has good effect in improving the accuracy of regional counting queries. The proposed algorithm has low computational complexity and obvious advantages in the publishing environment of big data.
关键词Location big data Privacy protection Adaptive density grids Hybrid decomposition Differential privacy
DOI10.1007/s10586-018-2125-z
收录类别SCI ; SCIE
语种英语
资助项目National Natural Science Foundation of China[61762059] ; Youth Science and Technology Project of Gansu Province[1310RJZA004]
WOS研究方向Computer Science
WOS类目Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS记录号WOS:000502007000155
出版者SPRINGER
EI入藏号20180904846951
EI主题词Data privacy
EI分类号723.2 Data Processing and Image Processing - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory
引用统计
被引频次:6[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://ir.lut.edu.cn/handle/2XXMBERH/64172
专题计算机与通信学院
电气工程与信息工程学院
通讯作者Yan, Yan
作者单位1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China;
2.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China
第一作者单位电气工程与信息工程学院;  兰州理工大学
通讯作者单位电气工程与信息工程学院;  兰州理工大学
第一作者的第一单位电气工程与信息工程学院
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GB/T 7714
Yan, Yan,Hao, Xiaohong,Zhang, Lianxiu. Hierarchical differential privacy hybrid decomposition algorithm for location big data[J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS,2019,22:S9269-S9280.
APA Yan, Yan,Hao, Xiaohong,&Zhang, Lianxiu.(2019).Hierarchical differential privacy hybrid decomposition algorithm for location big data.CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS,22,S9269-S9280.
MLA Yan, Yan,et al."Hierarchical differential privacy hybrid decomposition algorithm for location big data".CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 22(2019):S9269-S9280.
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