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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

DSC over TG (C = 1).
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig10: DSC over TG (C = 1).

Mentions: Figures 9 and 10 show the different space partitions over dataset TG of SHC and DSC, respectively. The blue line represents the space filling curves. We can see that SHC partitions the spatial domain using the unified granularity, while DSC partitions the spatial domain according to the density of POIs. For convenience of observation, we set N = 6 for SHC and set C = 1 for DSC.


Protecting location privacy for outsourced spatial data in cloud storage.

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

DSC over TG (C = 1).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig10: DSC over TG (C = 1).
Mentions: Figures 9 and 10 show the different space partitions over dataset TG of SHC and DSC, respectively. The blue line represents the space filling curves. We can see that SHC partitions the spatial domain using the unified granularity, while DSC partitions the spatial domain according to the density of POIs. For convenience of observation, we set N = 6 for SHC and set C = 1 for DSC.

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