Hierarchical differential privacy hybrid decomposition algorithm for location big data
Yan, Yan1,2; Hao, Xiaohong1; Zhang, Lianxiu2
2019-07
Source PublicationCLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
ISSN1386-7857
Volume22Pages:S9269-S9280
AbstractThe 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.
KeywordLocation big data Privacy protection Adaptive density grids Hybrid decomposition Differential privacy
DOI10.1007/s10586-018-2125-z
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61762059] ; Youth Science and Technology Project of Gansu Province[1310RJZA004]
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Theory & Methods
WOS IDWOS:000502007000155
PublisherSPRINGER
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.lut.edu.cn/handle/2XXMBERH/64172
Collection计算机与通信学院
电气工程与信息工程学院
Corresponding AuthorYan, Yan
Affiliation1.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
First Author AffilicationColl Elect & Informat Engn;  Lanzhou University of Technology
Corresponding Author AffilicationColl Elect & Informat Engn;  Lanzhou University of Technology
First Signature AffilicationColl Elect & Informat Engn
Recommended Citation
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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