Full Citation
Title: Geometric Algebra-Based Multilevel Declassification Method for Geographical Field Data
Citation Type: Book, Section
Publication Year: 2020
ISBN:
ISSN:
DOI: 10.1007/978-3-030-61864-3_43
NSFID:
PMCID:
PMID:
Abstract: The diversity of GIS application patterns leads to the demand for multilevel GIS data declassification. For example, Publicly used data must be declassified to hide confidential spatial information. The reversion process is not a common data permutation like the conventional encryption method does. The reverted data should also keep the general geospatial features. Furthermore, when facing different levels of confidentiality, different levels of reversion were needed. In this paper, A declassification and reversion method with controllable accuracy is realized using geometric algebra (GA). The geographical field is expressed as a GA object and the unified representation of the field is further realized. By introducing the rotor operator and perturbation matrix, the declassification methods are proposed for geographic field data, which can progressively revert the features of the field. A geometric algebraic declassification operator is also constructed to realize the unification operations of field features and spatial coordinate. By exploring the space error and space structure characterization of the results, a quantitative performance evaluation is provided. Experiments have shown that the method can carry out effective precision control and has good randomness and a high degree of freedom characteristics. The experimental data show a correlation coefficient of 0.945, 0.923 and 0.725 for the longitude-oriented field data during the low level, medium level and high level declassification, respectively. The algorithm characteristics meet the application needs of geographic field data in data disclosure, secure transmission, encapsulation storage, and other aspects.
Url: https://link.springer.com/chapter/10.1007/978-3-030-61864-3_43
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Authors: Luo, Wen; Li, Dongshuang; Yu, Zhaoyuan; Wang, Yun; Yan, Zhengjun; Yuan, Linwang
Editors:
Pages: 501-512
Volume Title: Advances in Computer Graphics
Publisher: Springer, Cham
Publisher Location: Geneva, Switzerland
Volume: 12221
Edition:
Data Collections: IPUMS NHGIS
Topics: Other
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