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Protecting location privacy for outsourced spatial data in cloud storage.

Tian F, Gui X, An J, Yang P, Zhao J, Zhang X - ScientificWorldJournal (2014)

Bottom Line: As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry.But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded.The evaluation results indicate that SHC(∗) and DSC are more secure than SHC, and DSC achieves the best index generation performance.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China ; Shaanxi Province Key Laboratory of Computer Network, Xi'an Jiaotong University, Xi'an 710049, China.

ABSTRACT
As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry. As a kind of spatial transformation method, Hilbert curve is widely used to protect the location privacy for spatial data. But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded. In this paper, we propose an index modification method for SHC (SHC(∗)) and a density-based space filling curve (DSC) to improve the security of SHC; they can partially violate the distance-preserving property of SHC, so as to achieve better security. We formally define the indistinguishability and attack model for measuring the privacy disclosure risk of spatial transformation methods. The evaluation results indicate that SHC(∗) and DSC are more secure than SHC, and DSC achieves the best index generation performance.

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Related in: MedlinePlus

Average number of POIs versus curve order.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig5: Average number of POIs versus curve order.

Mentions: SHC∗ has the same α as SHC, so we only study the relationship between curve parameters and α for SHC and DSC to guarantee that the following experiments are conducted under the same conditions. Figure 5 shows the α of SHC for different curve order N. The α of SHC drops drastically as curve order increases. When N = 12, α approaches 1 in NE, TG, and SF, but NA needs N = 13. Figure 6 depicts the α of DSC for different capacity C. The α of DSC rises slowly as capacity increases. When C = 1, α = 1. So we apply this setting to SHC (SHC∗ applies the same setting as SHC) and DSC in the remaining experiments.


Protecting location privacy for outsourced spatial data in cloud storage.

Tian F, Gui X, An J, Yang P, Zhao J, Zhang X - ScientificWorldJournal (2014)

Average number of POIs versus curve order.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4109601&req=5

fig5: Average number of POIs versus curve order.
Mentions: SHC∗ has the same α as SHC, so we only study the relationship between curve parameters and α for SHC and DSC to guarantee that the following experiments are conducted under the same conditions. Figure 5 shows the α of SHC for different curve order N. The α of SHC drops drastically as curve order increases. When N = 12, α approaches 1 in NE, TG, and SF, but NA needs N = 13. Figure 6 depicts the α of DSC for different capacity C. The α of DSC rises slowly as capacity increases. When C = 1, α = 1. So we apply this setting to SHC (SHC∗ applies the same setting as SHC) and DSC in the remaining experiments.

Bottom Line: As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry.But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded.The evaluation results indicate that SHC(∗) and DSC are more secure than SHC, and DSC achieves the best index generation performance.

View Article: PubMed Central - PubMed

Affiliation: School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China ; Shaanxi Province Key Laboratory of Computer Network, Xi'an Jiaotong University, Xi'an 710049, China.

ABSTRACT
As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry. As a kind of spatial transformation method, Hilbert curve is widely used to protect the location privacy for spatial data. But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded. In this paper, we propose an index modification method for SHC (SHC(∗)) and a density-based space filling curve (DSC) to improve the security of SHC; they can partially violate the distance-preserving property of SHC, so as to achieve better security. We formally define the indistinguishability and attack model for measuring the privacy disclosure risk of spatial transformation methods. The evaluation results indicate that SHC(∗) and DSC are more secure than SHC, and DSC achieves the best index generation performance.

Show MeSH
Related in: MedlinePlus