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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

Visualization of attacks.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig8: Visualization of attacks.

Mentions: Figure 8 depicts the estimated datasets reconstructed by the attack method over the POIs' indexes generated by SHC, SHC∗, and DSC, where a, b, c, and d denote the NE, TG, SF, and NA datasets, respectively, while 1, 2, 3, and 4 represent the original datasets, the estimated datasets over SHC, the estimated datasets over SHC∗, and the estimated datasets over DSC, respectively. From Figure 8, we can easily find out that the POIs' indexes generated by SHC∗ and DSC are more difficult to attack, because the estimated datasets are less similar to the original datasets. On the contrary, the estimated datasets reconstructed over the indexes built by SHC retain more details. It means that SHC∗ and DSC are more secure than SHC.


Protecting location privacy for outsourced spatial data in cloud storage.

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

Visualization of attacks.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig8: Visualization of attacks.
Mentions: Figure 8 depicts the estimated datasets reconstructed by the attack method over the POIs' indexes generated by SHC, SHC∗, and DSC, where a, b, c, and d denote the NE, TG, SF, and NA datasets, respectively, while 1, 2, 3, and 4 represent the original datasets, the estimated datasets over SHC, the estimated datasets over SHC∗, and the estimated datasets over DSC, respectively. From Figure 8, we can easily find out that the POIs' indexes generated by SHC∗ and DSC are more difficult to attack, because the estimated datasets are less similar to the original datasets. On the contrary, the estimated datasets reconstructed over the indexes built by SHC retain more details. It means that SHC∗ and DSC are more secure than SHC.

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