Hierarchical differential privacy hybrid decomposition algorithm for location big data

doi:10.1007/s10586-018-2125-z

	Hierarchical differential privacy hybrid decomposition algorithm for location big data
	Yan, Yan 1,2; Hao, Xiaohong 1; Zhang, Lianxiu 2
刊名	CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
	2019-07
卷号	22 页码:S9269-S9280
关键词	Location big data Privacy protection Adaptive density grids Hybrid decomposition Differential privacy
ISSN号	1386-7857
DOI	10.1007/s10586-018-2125-z
英文摘要	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.
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
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000502007000155
状态	已发表
内容类型	期刊论文
源URL	[http://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
推荐引用方式 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.