Institutional Repository of Coll Comp & Commun
| Hierarchical differential privacy hybrid decomposition algorithm for location big data | |
Yan, Yan1,2 ; Hao, Xiaohong1; Zhang, Lianxiu2
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| 2019-07 | |
| Source Publication | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
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| ISSN | 1386-7857 |
| Volume | 22Pages:S9269-S9280 |
| Abstract | 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. |
| Keyword | Location big data Privacy protection Adaptive density grids Hybrid decomposition Differential privacy |
| DOI | 10.1007/s10586-018-2125-z |
| Indexed By | SCI ; SCIE |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[61762059] ; Youth Science and Technology Project of Gansu Province[1310RJZA004] |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Information Systems ; Computer Science, Theory & Methods |
| WOS ID | WOS:000502007000155 |
| Publisher | SPRINGER |
| EI Accession Number | 20180904846951 |
| EI Keywords | Data privacy |
| EI Classification Number | 723.2 Data Processing and Image Processing - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://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 Author | Yan, Yan |
| Affiliation | 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 |
| First Author Affilication | Coll Elect & Informat Engn; Lanzhou University of Technology |
| Corresponding Author Affilication | Coll Elect & Informat Engn; Lanzhou University of Technology |
| First Signature Affilication | Coll 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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